estimating hedonic demand parameters in real estate market

Muğla Üniversitesi

Sosyal Bilimler Enstitüsü Dergisi (İLKE)

Bahar 2008 Sayı 20

ESTIMATING HEDONIC DEMAND PARAMETERS IN REAL

ESTATE MARKET: THE CASE OF MUGLA

Ercan BALDEMİR

Cüneyt Yenal KESBİÇ

Mustafa İNCİ

ABSTRACT

The affect of each characteristic of a heterogeneous good on its price can be determined

by Hedonic Price Models. This feature is originated from the assumption of the model that the

price of a heterogeneous good is the sum of the prices of the characteristics constituting that good.

Therefore, marginal prices for heterogeneous goods enter into the scene. In this respect, we

examine the marginal effects of different housing attributes on the selling price of houses in the

housing market in Mugla.

178 observations have been obtained through a questionnaire based on face to face

interviews with randomly selected real estate agencies from the urban districts of Mugla. The

analyses have been carried out by linear, logarithmic and logarithmic-linear functional forms,

which are frequently used in Hedonic Price Models. Considering the structure and features of

Mugla, the estimated coefficients are found to be significant in terms of both the characteristics of

housing and its location (its position, whether it is in-site or not, etc.). As expected, the variables

that positively affect the housing price have been found in all linear, logarithmic, and log-linear

models to be central heating, ceramic bathroom floor, location on the street, satellite TV,

hydrophor pump, modular kitchen, sunblind, solar water heating, front side facing south, 1500-

2000 meters to the city center, the number of bathrooms, square meter of housing, and elevator.

Key Words: Hedonic theory, house markets, hedonic price model.

Emlak Piyasasında Hedonik Talep Parametrelerinin Tahminlenmesi:

Muğla Örneği

ÖZET

Hedonik Fiyatlandırma Modeli ile heterojen bir malı oluşturan karakteristiklerin her

birinin fiyat üzerindeki etkisi tanımlanabilir. Bu durum, Model‘in, heterojen bir malın fiyatının,

onu oluşturan farklı niteliklerin piyasa fiyatlarının toplamından ibaret olduğunu varsaymasından

ileri gelir. Böylece heterojen mallar için marjinal fiyatlar söz konusu olmaktadır. Bu bağlamda,

çalışmada Muğla Konut Piyasasında konutların sahip olduğu farklı niteliklerin konut satış fiyatı

üzerindeki marjinal etkisi ortaya konmaya çalışılmıştır.

Muğla ili kentsel kesimde merkez ilçelerde emlak bürolarında emlakçılarla yüz yüze

görüşme suretiyle tesadüfi olarak 178 anket yapılmıştır. Analizler hedonik fiyat modelinde

sıklıkla benimsenen doğrusal, logaritmik ve logaritmik doğrusal fonksiyonlar kullanılarak

gerçekleştirilmiştir. Katsayı tahminleri gerek konutun özellikleri, gerekse konumu (konutun yeri,

site içinde olup olmaması vb.) açısından Muğla ilinin yapısı ve özellikleri dikkate alındığında

anlamlı çıkmıştır. Beklendiği gibi, doğrusal, logaritmik ve logaritmik doğrusal modellerin

Associate Professor, Mugla University, F.E.A.S., Department of Business Administration.

Associate Professor, Mugla University, F.E.A.S., Department of Economics.

Research Assistant, Mugla University, F.E.A.S., Department of Economics.

Estimating Hedonic Demand Parameters in Real Estate Market: The Case of Mugla

42

hepsinde konut satış fiyatını pozitif etkileyen değişkenler; merkezi kalorifer, seramik banyo

döşemesi, konutun sokakta bulunması, uydu sistemi, hidrofor, hazır mutfak, panjur, güneş

enerjisi, güney konumlu konut, şehir merkezine uzaklık 1500–2000 metre, banyo sayısı, konutun

metrekaresi, asansör sayısı olarak bulunmuştur.

Anahtar Kelimeler: Hedonik teori, konut piyasaları, hedonik fiyatlandırma modeli.

1. INTRODUCTION

Housing is a ―unique product‖ with three peculiarities (Harsman and

Quigley, 1991:2-3): (1) Complexity: Housing, as a kind of complicated goods,

can meet a great variety of a family‘s demands and be closely related to the

residents‘ activities such as life, work, amusement, etc.; (2) Fixity: Housing is

directly related to urban land in special location. The movement of housing is

basically impossible under the present technological conditions. This means that

the choice of housing involves consideration of neighborhood relations,

distance to the job site and corresponding public service facilities such as

schools, shopping centers, etc.; (3) Durability: This characteristic affects the

new housing market and stock housing market as well. Different from other

common commodity markets, housing market has a corresponding stock

market. Consumers can carry on replacement among new or old houses, choose

building type, community environment, degree of accessibility, and so on, to

meet individual preferences and get the greatest utility. These characteristics

indicate that influential factors of housing price are very complicated and

closely related to housing characteristics. Therefore, investigating the influence

factors of housing price inside the city from the viewpoint of housing

characteristics is a rational approach. In fact, since housing is a kind of

heterogeneous product, and there are obvious differences between housing

characteristics, scholars often establish hedonic price model to carry on

researches.

2. THE ECONOMIC THEORY OF HEDONIC PRICE MODEL

The term ―hedonic‖ is derived from Latin ―hedonikos‖, meaning

satisfaction. To this end, this concept is used in economics to imply for

enjoyment, satisfaction, pleasure or utility achieved with consumption of goods

or services (Kaul, 2006:4-5).

The term hedonic was first used in correcting price indices for quality

(Cowling and Cubbin, 1972:963). This term was used in an economic sense to

indicate that the index was computed taking into consideration not just the

objective aspects but also the qualitative utility obtained from a product (Kaul,

2006:4-5).

Hedonic Price Model depends on the consumer theory of the classical

economics, implying that each of the characteristics of heterogeneous goods

provides a different level of satisfaction or utility for the consumer. The model

suggests that the characteristics of a good meet different needs of consumers,

Ercan BALDEMİR-Cüneyt Yenal KESBİÇ-Mustafa İNCİ

43

and the satisfaction or utility level of the consumers differs with consumption of

each characteristic. That is why this kind of models carries the term ―hedonic‖

in their names with the meaning of enjoyment, satisfaction, pleasure or utility

obtained with consumption of goods and services.

The Hedonic Price Model was first introduced in 1939 by A.T. Court,

an expert of the American automobile industry (Bartik, 1987:81, Goodman,

1998:291). Court regarded automobile price as a function of the automobile‘s

different characteristics, and carried out hedonic price analysis of heterogeneous

goods. His ultimate objective was to structure the price index for the automobile

industry. After that, this method began to expand to other consumer goods, such

as tractors, washing machines, etc. Colwell and Dilmore believed that Haas was

one of the first users of the hedonic price model (the first hedonic model on

agriculture) (Colwell and Dilmore, 1999:620). Haas (1922) used the concept of

―hedonics‖, and set up a simple hedonic price model for farmland, taking the

distance to the city center and the city size as two important characteristic

variables. On the other hand, Ridker (1967) was one of the earliest scholars to

apply hedonic price theory to analyze the housing market. He calculated the

impact of improving environmental quality (such as the elimination of air

pollution) on housing price.

The theoretical foundation of the hedonic price model is generally

regarded as hedonic price theory. American researcher Lancaster (1966) first

came up with a new consumer theory. The theory was expanded from the

consumer theory of classical economics, also known as Lancaster preference

theory. From the product heterogeneity, Lancaster (1966) analyzed the basic

elements that formed the product, and argued that the demand for the product

was not based on the product itself, but on its characteristics. Heterogeneous

goods (especially such as housing) have a number of incorporated

characteristics, and the goods are sold as the gathering of inherent

characteristics. All of these characteristics are variables of the utility function of

the consumer. Therefore, the utility level depends on the quantity of different

characteristics. It is difficult to analyze a market of such goods with the

traditional economic model because it cannot be considered by a single total

price. Consequently, it is necessary to adopt a series of prices (hedonic price) to

express corresponding product characteristics. As a result, the price of the

product is made up of hedonic prices, with each product characteristic having its

own implied price.

On the other hand, American economist Rosen (1976) submitted, within

the context of Lancaster preference theory, the first equilibrium model of

market supply and demand based on product characteristics. Under the

condition of perfect competition market, with maximizing consumer‘s utility

and producer‘s profit as the goal, Rosen (1976) analyzed theoretically the long-

term and short-term equilibrium of the heterogeneous goods market.


44

The model identifies the goods )(Z as the total of their n

characteristics )( iZ . i contains n characteristics and indicates the quantity of

each characteristic. In this context, Rosen‘s model can be presented as follows

(Rosen, 1976:37):

)( iZfZ ),...,1( ni (1)

The goods are described by numerical values of Z and provide the

consumers with different packages of characteristics. Moreover, existence of

product differentiation enabled by the presence of diverse characteristics

implies that a wide variety of alternative packages are available. Accordingly,

the demand function can be described with respect to price and characteristics

as follows:

),...,,()( 21 nzzzpzP (2)

This function reveals the hedonic price regression obtained from

comparing prices of brands with different characteristics. In other words, it

gives the minimum price of any combination of characteristics. If two brands

offer the same combination but with different prices, consumers chooses the

cheaper one, and the identity of sellers does not have any affect on their

demand. In this connection, taking the partial derivatives of Equation 2, the

corresponding effect of each characteristic on the price (hedonic price) can be

expressed as follows:

İ

ZiZ

PP

(3)

Lancaster and Rosen‘s approaches try to estimate the combinations of

characteristics –measured objectively and affect the utility– that are comprised

of a number of attributes that the consumer appraises. However, these models

have some basic differences. Lancaster‘s model assumes that the goods are

members of a group, and the goods in a group consist of combinations of

characteristics in accordance with the budget constraint. On the other hand,

Rosen‘s model suggests that goods are in preferential order but consumers are

indifferent for the characteristics while buying a combination of goods.

Moreover, each good is chosen from a bundle of brands and consumed in

certain periods of time. Therefore, Lancaster‘s approach is suitable for all

consumption goods while Rosen‘s model is appropriate for only durable

consumption goods.

Unlike Lancaster, Rosen points out a nonlinear relation between the

price and the inner characteristics of goods. Nonlinearity of the price function in

this model implies that the implicit prices are inconstant.

Rosen‘s model includes two different stages. The first stage determines

the characteristics that affect the price of the good and estimates the marginal


45

prices for them. This stage develops a price measure but does not directly

provide an inverse demand function. It only reveals the marginal contributions

of the characteristics to the price. The second stage defines the inverse demand

function or estimates the marginal demand function through the implicit price

function determined in the first stage.

According to Rosen, the consumer immediately adjusts the budget

constraint for an increase in his/her income, and his/her marginal demand for a

characteristic may change. Rosen assumes that the price the consumer is willing

to pay for a good –or a combination of characteristics– is a function of the

variables that affect consumers‘ pleasure and preferences such as the

consumer‘s utility level, income, age, and education.

Rosen argues that the inverse demand function can be estimated in the

second stage by simultaneous equations using the marginal price as endogenous

variable, and that the inverse demand function is based on the implicit marginal

cost function. However, this identification of the inverse demand function may

be problematic. If the supply of the good has perfect elasticity or the supply of

characteristics is fixed, the marginal price of the characteristics will be

exogenous in the estimation of the inverse demand function. Therefore, Bartik

(1987) opposes Rosen‘s approach to the hedonic price model and argues that

the problem in hedonic estimation is not a result of the interaction between

supply and demand since an individual consumer cannot affect the sellers.

Under a nonlinear budget constraint, the endogeneity of all marginal prices and

the quantity of the characteristics result in hedonic estimation problem. For that

reason, there is no need to include the supply side of the market in the model. In

this regard, the low elasticity of supply of housing in Mugla –as a result of, inter

alia, the scarcity and therefore the high prices of lands for housing– and the

large quantity of the characteristics of housing require that marginal prices of

the characteristics be considered as endogenous. Accordingly, our study focuses

on the first stage of Rosen‘s model and aims to identify the marginal effects of

various characteristics on the housing price in the housing market of Mugla.

3. LITERATURE ON HEDONIC PRICE MODELS IN HOUSING

MARKET

Application of the hedonic price theory to the housing market was, as

mentioned above, first introduced by Ridker and Henning (1967), who analyzed

the effect of air pollution on housing prices. Following this study, a number of

empirical studies appeared in the hedonic price literature regarding the housing

market, a brief list of which may include Kain and Quigley (1970), Straszheim

(1973, 1974), Goodman (1978), Witte, Sumka and Erekson (1979), Palmquist

(1984), Mendelsohn (1984), Blackley, Follain and Lee (1986), Goodman

(1988), Meese and Wallace (1991), Kim (1992), Macedo (1996), Can and

Megbolugbe (1997), Meese and Wallace (1997), Powe, Garrod, Brunsdan and

Willis (1997), Yang (2000), Leishman (2001), Ucdogruk (2001), Bover and


46

Velilla (2002), Ogwang and Wang (2002), Wilhemsson (2002), Toda and

Nozdrina (2002), Maurer, Pitzer and Sebastian (2004), Wen, Lu and Lin (2004),

Filho and Bin (2005), Cohen and Coughlin (2005), Yankaya and Celik (2005),

Hai-Zhen, Sheng-Hua and Xiao-Yu (2005), Li, Prud‘Homme and Yu (2006).

The variables and functional forms they used and their findings are

chronologically presented in the appendix.

4. HEDONIC PRICE MODELS FOR THE HOUSING MARKET OF

MUGLA

Data for this study has been gathered for the reference period –May

2007– through a questionnaire of 33 questions on housing prices and 32

characteristics which are considered to have had an effect on them. 178

observations have been obtained through a questionnaire based on face to face

interviews with randomly selected real estate agencies from the urban districts

of Mugla. These observations consists 100 percent of the population. This data

has been analyzed through the hedonic price approach. The variables have been

selected with reference to the literature.

Real estate agencies were asked questions regarding the number of

balconies, the number of elevators, the number of houses in the apartment, the

size of the house, the number of rooms, the floor level, the age of the house

(continuous variable), the heating system, the flooring of the living room and

other rooms, the flooring of the bathroom, the material of windows‘ frames,

roof isolation, wall covering, location, structure of the kitchen, satellite TV,

hydrophor pump, parking lot, sunblind, solar water heating, doorman, whether it

is in-garden and in-site, distance to the city centre, direction of the front side,

ground survey, and occupancy (proxy variable).

There are three most frequently used function forms in hedonic price

model: linear, logarithmic, and log-linear. This study utilizes all the three forms

and interprets the common significant variables. The variables have been

analyzed under SPSS 10.0 statistical program, their frequencies have been

determined, and the variables found to be problematic have been excluded from

the analysis.

Table 1 presents the means and standard deviations of the variables.

Average housing price in the City Center of Mugla is 119.240 YTL (New

Turkish Lira). Average number of bathrooms is 1 while that of balconies is 2.

On the other hand, the average number of rooms in the houses questioned is

3.54. The average number of dwellings in an apartment building where a house

in question exists is 11.5 while their average age is approximately 11. In

addition, Table 1 indicates that the houses in the city center of Mugla are on

average 119 meter square and on the 2nd

or 3rd

floor. 60 percent of the houses

are located on streets while 48 percent of them are located on corners, and 53

percent of them are with front side to south. 53 percent of the houses are as far

as 500-1000 meters to the city center. 39 percent of the houses are in buildings


47

in which owners of the houses live. Furthermore, 93 percent of the houses are

with clay-tile roofs, 54 percent are with central heating system (furnace) while

34 percent of them are heated with stoves.

Table 1: The Mean and Standard Deviation of Housing Prices and the

Variables that are considered to Have an effect on the Housing Prices

Variable Mean Std. Dev.

Variable Mean

Std. Dev.

Housing Price 119.24 33.427 Age of House 10.94 7.457

Hea

ting

Stove .34 .474

Dis

tan

ce t

o

Cit

y C

entr

e

500–1000m .53 .501

Central (Floor) .12 .323 1000-1500m .23 .422

Central

(Apartment) .54 .499

1500-2000m .16 .365

Other .00 .000 >2000m .08 .279

Liv

ing

Roo

m F

loo

r

Stone Tile .07 .251

Occ

up

ancy

Vacant .29 .453

Prefinished

Hardwood .57 .497

Tenant .33 .470

Unfinished Hardwood

.27 .445

Owner .39 .490

Ceramic .04 .195

Laminate .02 .129

Fro

nt

side

to

North .17 .380

Carpet .01 .106 South .53 .501

Other .03 .181 East .24 .429

Roo

m F

loo

r

Stone Tile .05 .220 West .22 .415

Prefinished

Hardwood .54 .500

Th

e N

um

ber

of

Dwellings (in

apt.) 11.51 8.009

Unfinished

Hardwood .31 .463

Rooms 3.54 .648

Ceramic .04 .195 Bathrooms 1.07 .251

Laminate .02 .129 Balconies 1.93 .737

Carpet .01 .106 Elevators .28 .451

Other .04 .195

Lo

cati

on Street .60 .491

Bat

hro

om

Flo

or

Stone Tile .02 .129 Main Street .39 .490

Glazed tile .44 .498 Avenue .03 .181

Ceramic .54 .499 On the Corner .48 .501

Other .01 .075 Ground Survey .53 .501

Win

do

w

Fra

mes

Wooden .11 .317 Sunblind .08 .270

Aluminum .22 .419 Solar Water Heating .28 .451

PVC .66 .476 Located in-Site .33 .470

Other .01 .075 Garden .61 .490

Roo

f Betony .06 .241 Ventilation .70 .459

Clay Tile .93 .251 Meter Square 119.97 24.772

Profiled Sheeting .02 .129 Floor Level 2.63 1.339

Wal

l

Plastic Paint .56 .498 Fire Exit .20 .399

Oil Paint .07 .251 Satellite TV .27 .445

Satin Paint .37 .483 Doorman .37 .483

Wallpaper .00 .000 Hydrophor Pump .62 .486

Modular Kitchen .45 .499 Parking Lot .43 .496


48

This study estimates the hedonic price model through the E-Views

program. To find out the most suitable model, the study utilizes the most

commonly used top-to-down or general-to-specific approaches (Gujarati 1999)

of Hendry (1985) or the so-called London School of Economics (LSE)

Approach. The initial model covered all of the variables, and then the most

insignificant ones were removed from the model in order until a significance

level of 20.0 was achieved, with the following variables: heating by stove;

living room floor–stone tile; bathroom floor–stone tile; wooden window frames;

betony roof; walls–oil paint; house on the corner; house vacant; house with

front side to north; distance to city center-500–1000m. The variables that were

found to be significant and the related models are presented in Table 2. The

existence of heteroskedasticity in the models was tested via Breusch-Pagan-

Godfrey test and rejected at 05.0 .

Table 2: Results for the Linear, Logarithmic, and Log-linear Models

Variables Linear Model Log-linear Model Log-Log Model

Coefficient Significance Coefficient Significance Coefficient Significance

Central Heating

(Apartment) 12.81457 0.0001 0.117066 0.0000 0.124027 0.0000

Living Room Floor:

unfinished hardwood 5.819712 0.1690 - - - -

Living Room Floor:

Ceramic - - -0.102628 0.0471 -0.093858 0.0638

Bathroom Floor Ceramic 5.101686 0.0498 0.051819 0.0133 0.042196 0.0439

On the Street 4.860699 0.0676 0.035469 0.1235 0.035097 0.1178

Satellite TV 6.782905 0.0538 0.070542 0.0133 0.048301 0.0790

Hidrophor Pump 5.275722 0.0741 0.053024 0.0239 0.054837 0.0184

Parking Lot 3.732982 0.2059 - - 0.029643 0.2226

Modular Kitchen 3.550859 0.1859 0.039558 0.0653 0.029484 0.1641

Sunblind 12.72444 0.0096 0.093856 0.0130 0.098895 0.0074

Solar Water Heating 4.272688 0.1716 0.032140 0.1912 0.039805 0.1019

In-Site -11.02580 0.0003 -0.080453 0.0007 -0.078754 0.0010

Garden - - -0.030564 0.1836 -0.037945 0.1168

Fire Exit - - -0.045414 0.1327 - -

Occupied by Tenant -5.529069 0.0951 - - - -

Occupied by Owner -5.081245 0.1115 - - - -

Front Side to South 5.327686 0.0500 0.044006 0.0422 0.038325 0.0724

Front Side to West -5.490518 0.0830 -0.044016 0.0823 -0.042196 0.0871

On the Corner -3.326854 0.1979 - - - -

Distance to City Centre:

1500-2000m 7.787394 0.0269 0.065401 0.0231 0.066403 0.0175

The Number of Bathrooms 14.86537 0.0054 0.063349 0.1352 0.082357 0.0462

The Number of Rooms -4.772347 0.0615 - - -0.106115 0.1115

Meter Square 0.721088 0.0000 0.005275 0.0000 0.691436 0.0000

The Number of Elevators 13.83910 0.0001 0.104916 0.0002 0.097921 0.0003

The Number of Dwellings

in the Building 0.234487 0.1363 0.002469 0.0482 - -

The Age of the Building - - -0.002554 0.1069 -0.016317 0.1736

F Value 28.86337 0.000000 34.92061 0.000000 36.63798 0.000000

R2/Adjusted R2 0.811703 0.783581 0.816463 0.793082 0.823548 0.801070

Ø 0.424913 6.270495 4.94169

2m-1

12.3380 10.1175 10.8108


49

5. INTERPRETATION AND SIGNIFICANCE OF THE COEFFICIENTS

Following are the findings from the hedonic model estimated for Mugla

Center:

Central heating in the apartment, rather than a stove, increases the

hedonic price of the house 12.8 unit in the linear model, 11% in the log-

linear model, and 0.12% in the log-log model. Moreover, the coefficient

of the central heating is positive and significant at 1% in all the three

models.

Ceramic tile floor in the bathroom, rather than stone tile, increases the

hedonic price of the house 5.1 unit in the linear model, 5% in the log-

linear model, and 0.04% in the log-log model. The coefficient of the

ceramic tile floor in the bathroom is positive and significant at 5% in all

the three models.

Location on the street increases the hedonic price of the house 4.8 unit

in the linear model, 3% in the log-linear model, and 0.03% in the log-

log model. The coefficient of location on the street is positive in all the

three models but significant only in the linear model at 10%. In the

other models, this variable does not have any affect on the hedonic

price.

Satellite TV increases the hedonic price of the house 6.78 unit in the

linear model, 7% in the log-linear model, and 0.04% in the log-log

model. The coefficient of satellite TV is positive in all the three models

and significant at 5% in the linear model and at 10% in the other

models.

Hydrophor pump increases the hedonic price of the house 5.27 unit in

the linear model, 5% in the log-linear model, and 0.05% in the log-log

model. The coefficient of hydrophor pump is positive in all the three

models and significant at 10% in the linear model and at 5% in the

other models.

Sunblind increases the hedonic price of the house 12.72 unit in the

linear model, 9% in the log-linear model, and 0.09% in the log-log

model. The coefficient of sunblind is positive in all the three models

and significant at 5% in the log-log model and at 1% in the other

models.

The coefficient of location in a site is negative and significant at 1% in

all the three models. This variable decreases the hedonic price of the

house 11 unit in the linear model, 8% in the log-linear model, and

0.07% in the log-log model.

Front side to south, rather than to north, increases the hedonic price of

the house 5.32 unit in the linear model, 4% in the log-linear model, and

0.03% in the log-log model. The coefficient of this variable is positive


50

in all the three models and significant at 10% in the linear and log-log

models and at 5% in the log-linear model.

Front side to west, rather than to north, increases the hedonic price of

the house 5.49 unit in the linear model, 4% in the log-linear model, and

0.042% in the log-log model. The coefficient of this variable is negative

and significant at 10% in all the three models.

A distance of 1500-2000m to the city center, compared to that of 500-

1000m, increases the hedonic price of the house 7.78 unit in the linear

model, 6% in the log-linear model, and 0.06% in the log-log model.

The coefficient of this variable is positive and significant at 5% in all

the three models.

One-unit increase in the number of bathrooms increases the hedonic

price of the house 14.86 unit in the linear model, and 6% in the log-

linear model. On the other hand, 1% increase in the number of

bathrooms increases the hedonic price 0.08% in the log-log model. The

coefficient of this variable is positive in all the three models and

significant at 1% in the linear model and 5% in the log-log model,

while it is insignificant in the log-linear model.

One-unit increase in the meter square of the house increases its hedonic

price 0.72 unit in the linear model, and 0.5% in the log-linear model.

On the other hand, 1% increase in the meter square of the house

increases its hedonic price 0.06% in the log-log model. The coefficient

of this variable is positive and significant at 1% in all the three models.

One-unit increase in the number of elevators in the building increases

the hedonic price of the house 13.83 unit in the linear model, and 10%

in the log-linear model. On the other hand, 1% increase in the meter

square of the house increases its hedonic price 0.09% in the log-log

model. The coefficient of this variable is positive and significant at 1%

in all the three models.

F values regarding the findings above are significant at 01.0 level.

In addition, 2R values indicate that these variables explain approximately 81%

of the change in the hedonic price of the house.

6. CONCLUSION

The findings revealed by the econometric model in this study on

estimating hedonic price parameters in the real estate market in Mugla province

have met the theoretical and economic expectations. In other words, considering

the structure and features of Mugla, the estimated coefficients are found to be

significant in terms of both the characteristics of housing and its location (its

position, whether it is in-site or not, etc.).


51

As expected, the variables that positively affect the housing price have

been found in all linear, logarithmic, and log-linear models to be central

heating, ceramic bathroom floor, location on the street, satellite TV, hydrophor

pump, modular kitchen, sunblind, solar water heating, front side facing south,

1500-2000 meters to the city center, the number of bathrooms, square meter of

housing, and elevator.

The affect of particularly two variables on housing prices needs to be

interpreted taking into account the structure and characteristics of Mugla.

In-Site Location of Housing: The analysis has revealed by all the three

models that in-site location of housing negatively affect the price of housing,

implying that in-site location decreases the price of housing. The reason

underlying this argument may be that housing in Mugla is mostly in cooperative

type, and their prices are relatively lower, which is attributable to the common

opinion that the material and workmanship used in this kind of housing is of

low quality.

Distance to the City Center (1500–2000m): Our findings suggest that

the price of houses is higher if distance to the city center is 1500–2000 meters.

This is mainly because the closer areas, up to an approximate distance of 2 km,

are not allowed for construction in Mugla. That is why such a distance is

considered close to the city center, and in this manner housing tends to become

more expensive as it gets closer to such a distance.

This study has the merit of identifying the factors that may affect the

housing prices in Mugla at present and in the future by means of hedonic

models, providing a data set on the real estate market for both buyers and

sellers.

7. REFERENCES

Bartik, T. J. (1987). ―The Estimation of Demand Parameters in Hedonic Price

Models‖, The Journal Of Political Economy, 95(1): 81-88.

Blackley, D.M., J.R. Follain and H.Lee. (1986). ―An Evaluation of Hedonic

Price Indexes for Thirty Four Large SMSAs‖, AREUEA Journal, 14(2):

179-205.

Bover, O. and P.Velilla. (2002). ―Hedonic House Prices Without

Characteristics: The Case of New Multiunit Housing‖, European

Central Bank Working Paper Series, Working Paper No.117.

Can, A. and I. Megbolugbe. (1997). ―Spatial Dependence and House Price

Index Construction‖, Journal of Real Estate Finance and Economics,

14: 203–222.

Cohen, J. P. and C. C. Coughlin. (2006). ―Airport-Related Noise, Proximity,

and Housing Prices in Atlanta‖, Federal Reserve Bank of St. Louis

Working Paper , Working Paper 060B.


52

Colwell, P.F. and G. Dilmore. (1999). ―Who Was First? An Examination of an

Early Hedonic Study‖, Land Economics, 75(4): 620-626.

Court, A. T. (1939). Hedonic Price Indexes with Automotive Examples in

Dynamic of Automobile Demand, General Motors, New York.

Cowling, K. and J. Cubbin. (1972). ―Hedonic Price Indexes for United Kingdom

Cars‖, Economic Journal, 82 (September): 963-978.

Filho, C. M. and O. Bin. (2005). ―Estimation of Hedonic Price Functions via

Additive Nonparametric Regression‖, Empirical Economics, 30: 93–

114.

Goodman, A. C. (1978). ―Hedonic Prices, Price Indices, and Housing Markets‖,

Journal of Urban Economics, 5(4): 471-84.

Goodman, A. C. (1988). ―An Econometric Model of Housing Price, Permanent

Income, Tenure Choice, and Housing Demand‖, Journal of Urban

Economics, 23: 327-353.

Goodman, A. C. (1998). ―Andrew Court and the Invention of Hedonic Price

Analyzis‖, Journal of Urban Economics, 44(2): 291-298.

Gujarati, D.N. (1999). Temel Ekonometri, Çev:Ü.Şenesen ve G:G:Şenesen,

Literatür Yayıncılık, İstanbul.

Harsman, B. and J.M. Quigley. (1991). Housing Markets and Housing

Institutions: An International Comparison,. Kluwer Academic Press,

Boston.

Kain, J. F. and J. M. Quigley. (1970). ―Measuring the Value of Housing

Quality‖, Journal Of The American Statistical Association, 65 (330):

532-548.

Kaul, S. (2006). Hedonism and Culture: Impact on Shopper Behaviour, Indian

Institute of Management Publication.

Kim, S. (1992). ―Search, Hedonic Prices and Housing Demand‖, The Review of

Economics and Statistics, 74(3) : 503-508.

Lancaster, K. J. (1966). ―A New Approach to Consumer Theory‖, Journal of

Political Economy, 74: 132-57.

Leishman, C. (2001). ―House Building and Product Differentiation: An Hedonic

Price Approach‖, Journal of Housing and the Built Environment, 16:

131–152.

Li, W., Prud‘Homme M. and Yu K. (2006). Studies in Hedonic Resale Housing

Price Indexe, OECD-IMF Workshop.

Macedo, R. (1996). ―Hedonic Price Models with Spatial Effects: An

Application to the Housing Market of Belo Horizonte, Brazil‖, Revista

Brasileira de Economia, 29(3): 343-365.


53

Maurer, R., M. Pitzer and S. Sebastian. (2004). ―Hedonic Price Indices for the

Paris Housing Market‖, Allgemeines Statistisches Archiv, 88: 303-326.

Meese, R. and N. Wallace. (1991). ―Nonparametric Estimation of Dynamic

Hedonic Price Models and the Constructions of Residential Housing

Price Indices‖, AREUEA Journal, 19(3):308-332.

Meese, R. and N. Wallace. (1997). "The Construction of Residential Housing

Price Indices: A Comparison of Repeat-Sales, Hedonic-Regression, and

Hybrid Approaches", Journal of Real Estate Finance and Economics,

14: 51–73.

Mendelsohn, R. (1984). ―Estimating the Structural Equations of Implicit

Markets and Household Production Functions‖, The Review of

Economics and Statistics, 66(4): 673-677.

Ogwang, T. and B. Wang. (2002). ―A Hedonic Price Function for a Northern

BC Community‖, Social Indicators Research, 61: 285-296.

Palmquist, R. B. (1984). ―Estimating the Demand for the Characteristics of

Housing‖, Review of Economics and Statistics, 66(3): 394-404.

Powe, N.A., Garrod G.D., Brunsdon C.F. and K.G. Willis. (1997). ―Using a

Geographic Information System to Estimate a Hedonic Price Model of

the Benefits of Woodland Access‖, Forestry, 70(21): 39-149.

Ramanathan, R. (1998). Introductory Econometrics with Applications, The

Dryden Press, Fourth Edition.

Ridker, R.G. and J.A. Henning. (1967). ―The Determinants of Residential

Property Values with Special Reference to Air Pollution‖ The Review of

Economics and Statistics, 49: 246–257.

Rosen, S. (1974). ―Hedonic Prices and Implicit Markets: Product

Differentiation in Pure Competition‖, Journal of Political Economy, 82:

34-55.

Straszheim, M.R. (1973). ―Estimation Of The Demand For Urban Housing

Services From Household Interview Data‖, Review of Economics and

Statistics, 55: 1-8.

Straszheim, M.R. (1974). ―Hedonic Estimation of Housing Market Prices: A

Further Comment‖, The Review of Economics and Statistics, 56(3):

404-406.

Toda, Y. and N. N. Nozdrina. (2004). ―The Spatial Distribution of the

Apartment Prices In Moscow in 2002: Hedonic Estimation from Micro

Data‖ ENHR Conference July 2nd-6th 2004, Cambridge.

Üçdoğruk, Ş. (2001). ―İzmir İlinde Emlak Fiyatlarına Etki Eden Faktörler:

Hedonik Yaklaşım‖, D.E.Ü. İ.İ.B.F. Dergisi, 16(2): 149-161.

Wallace, H.A. (1926). ―Comparative Farmland Values in Iowa‖, Journal of

Land and Public Utility Economics, 2: 385-392.


54

Wen H., J. Sheng-Hua and G. Xiao-Yu. (2005). ―Hedonic Price Analysis of

Urban Housing: An Empirical Research on Hangzhou, China‖, Journal

of Zhejiang University Science, 6A (8):907-914.

Wen, H.Z., J.F. Lu, and L. Lin. (2004). An Improved Method of Real Estate

Evaluation Based on Hedonic Price Model, International Management

Conference.

Wilhelmsson, M. (2002). ―Household Expenditure Patterns for Housing

Attributes: A Linear Expenditure System with Hedonic Prices‖, Journal

of Housing Economics, 11: 75–93.

Witte, A. D., H. Sumka and J. Erekson. (1979). ―An Estimate of a Structural

Hedonic Price Model of the Housing Market: An Application of

Rosen's Theory of Implicit Markets‖, Econometrica, 47: 1151-72.

Yang, Z. (2000). ―An Application of the Hedonic Price Model with Uncertain

Attribute: The Case of the People's Republic of China‖, Property

Management, 19(1): 50-63.

Yankaya, U. ve M. Çelik. (20059. ―İzmir Metrosunun Konut Fiyatları

Üzerindeki Etkilerinin Hedonik Fiyat Yöntemi İle Modellenmesi‖,

D.E.Ü. İ.İ.B.F. Dergisi, 20(2): 61-79


55

Appendix: Examples of Hedonic Pricing Models in House Markets

Study,

Data and Functional

Form

Variables Conclusions and Evaluation

Ridker

&

Henning

(1967) , 167

Observation

About House

Selling, Linear

Functional

Form

Dependent Variable: Median value

of owner-occupied single family

housing units

Independent Variables:

(1) An index of annual geometric

mean sulfation levels, (2) Median

number of rooms per housing unit,

(3) Percentage recently built, (4)

Total houses per square mile of

tracts, (5) Time zone for central

business district, (6) Percentage non-

white housing units, (7) School

quality, (8) Occupation ratio, (9)

Highway accessibility, (10) İllionis/

Missouri dummy variable, (11)

Persons per unit, (12) Median family

income, (13) Index of annual

geometric mean concentrations of

suspended particulates gathered by

high-volume air samplers, (14)

Percentage substandart, (15) Crime

rate, (16) Shopping area accessibility,

(17) Industrial area accessibility, (18)

Social area analysis indexes.

—This study was one of the earliest to apply hedonic price

theory for analyze the housing market and calculated the

impact of improving environmental quality on housing

price.

—The variables which causes multicollinearity problem

also featured in the study and introduced the results if

including these varibles or not. Thus, adjustments for

multicollinearity choosing four different estimating

method.

—The most important results are statistically significant

and all are fairly reasonable within the context of the area.

—Sulfation levels to which any single-family dwelling

unit is exposed were to drop by 0.25 mg./100cm2/day, the

value of that property could be expected to rise by at least

$83 and more likely closer to $245.23.

—Characteristics specific to the property [variable (2),(3)

and (4)] all turned out to be important explanatory

variables. The sign and magnitudes of their coefficients are

as expected.

— Both variables (5) and (9) are statistically significant.

The coefficients attached to variable (5) , however, are not

quite as expected.

—Variable (8) proved to be best estimated among

neighbourhood characteristics. The coefficients of variable

(7) are positive.

Kain

&

Quigley

(1970) , 1184

Observation In

The Entire

Model And

854

Observation

For The

Restricted

Model

About House

Selling,,Semi-

Logarithmic

and Linear

Functional

Forms

Dependent Variable: Dwelling unit

price


(1) Basic residential quality, (2)

Dwelling unit quality, (3) Quality of

proximate properties, (4)

Nonresidential usage, (5) Avarage

structure quality, (6) Proportion

white in census tract, (7) Median

schooling of adults in census tract,

(8) Public School achievement, (9)

Number of major crimes, (10) Age of

structure, (11) Number of rooms

(natural log.), (12) Number of

bathrooms, (13) Parcel area

(hundreds of sq. ft.), (14) First flor

area (hundreds of sq. ft.), (15) Single

detached, (16) Duplex, (17) Row,

(18) Apartment, (19) Rooming

house, (20) Flat, (21) No heat

included in rent, (22) No water

included in rent, (23) No major

appliances included in rent, (24) No

furniture included in rent, (25) Hot

water, (26) Central heat, (27)

Duration of occupancy (years), (28)

Owner in building

—This article estimates the market value, or the implicit

prices of specific aspects of the bundles of residential

services consumed by urban households. Quantitative

estimates were obtained by regressing market price of

owner-and renter-occupied dwelling units on measures of

the qualitative and quantitative dimensions of the housing

bundles.

—The measures of residential quality obtained by using

factor analysis to aggregate some 39 indexes of the quality.

—For renters equations used 25 variable and for owners

equations used 15 variable in the study.

—The analysis indicates that the quality of the bundles of

residential services has about as much effect on the price

of housing as such objective aspects as the number of

rooms, number of bathrooms, and lot size.

—For renters, among the first 5 quality variables, variable

(1), (2) and (5) are statistically significiant in the model

which have restristed observation. For owners, among the

first 5 quality variables, variable (3) and (5) are not

statistically significiant in the model which have restristed

observation. For renters equations only 16 variable and for

owners equations only 5 variable are statistically

significiant at %5 significance level.

—The most striking difference when the model is

reestimated for the entire obvervations is the increase in

the significance of the coefficients.


56

Study,

Data and Functional Form


Straszheim

(1973), Household

Interview Data For

1965

(100-200

Observation About

House Selling),

Linear Functional

Form

Dependent Variable: Price of

standartized dwelling unit


(1) Probability of ownership, (2)

Number of rooms in dwelling

units, (3) Structure age dummies,

(4) Lot size dummies, (5)

Structure condition dummies, (6)

Unsound condition dummies, (7)

Sample size

—Separate equations were estimated for owner and rental

units, and for each geographic submarket.

—It was found strong relationship between house price

and variables (3), (4) and (7).

— Variables (3) and (4) were always statistically

significiant.

—Analysis of covariance tests reveal satatistically

significiant differences in the equations across zones.

—There is substantial spatial variation in the price of most

attributes of housing services.

Straszheim

(1974), Pooled

Data Of The 3

Different District

About House

Characteristics,

Linear Functional

Form

Dependent Variable: House

selling price


(1) Number of rooms, (2) Built in

pre1940, (3) Built in 1940–1945,

(4) Built in 1950–1959, (5) Lot

size less than 2 acre, (6) Lot size

between 3–5 acre, (7) Lot size

greater than 5 acre, (8) Unsound

condition dummy , (9) Sample

size

—F-tests reveal that the geographic stratification reduces

the residual sum of squared errors. A few of the individual

submarket equations are presented to illustrate the range of

estimates obtained.

—Though suburban submarkets exhibit more price

homogeneity, there are also limits to how wide a

geographic area can be employed.

—The discussion of hedonic price estimation might more

usefully be directed to the criteria which should be

employed to define homogenous submarkets within urban

areas.

Goodman

(1978), 1835

Observation About

House Selling,

Box-Cox

Functional Form

Dependent Variable: House

selling price


(1) Lot size in sq. ft., (2) 1 if house

is all brick; 0 otherwise, (3) 1 if

hardwood floors; o otherwise, (4)

Number of covered garage spaces,

(5) Age of house in years, (6)

Number of rooms excluding

bathrooms, lavatories, (7) Number

of full bathrooms, (8) Number of

Lavatories, (9) Indoor living space

in sq. Ft., (10) Number of

fireplaces, (11) Percentage balack

population, (12) Percentage

families with income less than

5000 $, (13) Percentage of

population over age 25 with 13 or

more years of education, (14) 1 if

black is greater than %5 and less

than %15, (15) Principle

components measure of

neighbourhood attitudes

— This study appears to clarify several aspects of housing

analysis using hedonic prices, with respect to market

segmentation, functional form and behavior of prices

within submarkets. In positing various spatially and

temporally separate submarkets, covariance analysis

indicates heterogeneity of coefficients.

-—Model results showed that, Variables affect the house

prices differently in urban and suburb areas and for both

structure and neighbourhood characteristics the price are

up to 20% higher than the suburbs.

— Intrametropolitan examination of structural and

neighborhood quality reveals that the relative valuation of

physical improvements in housing is smaller in the central

city than in the suburbs, while the relative valuation of

improved neighborhoods is relatively constant.

-— Aggregation of hedonic price coefficients into

standardized units yields significantly higher housing

prices in the central city than in its suburbs, as well as

differential effects of structural and neighborhood

improvements among submarkets.


57

Study,

Data and

Functional Form

Variables Conclusions and

Evaluation

Witte

&

Sumka

&

Erekson

(1979), 500

Observation

About House

Rents In Four

City, Linear

Functional Form

Dependent Variable: House rent

Independent Variable (For Consumers): (1) Total annual current

income of household, (2) Age in years of the head of household, (3)

1 If the head of household is employed in blue collar job; 0

otherwise, (4) 1 If the head of household is employed in white collar

job; 0 otherwise, (5) 1 if the highest level of education attained by

head of household is high school; 0 otherwise, (6) 1 if the head of

household has education beyond the high school level; 0 otherwise,

(7) The number of persons in the household, (8) 1 if the household

head is female and 0 if male.

Independent Variable (For Supplier): (1) The number of years the

landlord has owned the dwelling, (2) 1 if there is a lease; 0

otherwise, (3) 1 if the family has lived in the dwelling for five years

or more; 0 otherwise, (4) 1 if the landlord is resident in the dwelling;

0 otherwise, (5) 1 if rent is collected more often than once a month;

0 otherwise, (6) 1 if a professional manager is employed; 0

otherwise, (7) The number of rental units owned, (8) 1 if ownership

is of the corporate form; 0 otherwise, (9) 1 if the rental housing

owned was acquired by inheritance; 0 otherwise, (10) 1 if the head

of household is black; 0 otherwise.

Other Independent Variables: (1) Dwelling quality, (2) Dwelling

size, (3) Lot size, (4) Neighbourhood quality, (5) A measure of

accessibility

—In this study, estimated a

simultaneous system of hedonic

price equations suggested by the

work of Rosen. This system

consisted of bid and offer curves

for each of housing bundle

attributes, dwelling quality,

dwelling size, and lot size.

—Empirical results confirm the

theoretically expected negative

coefficient for each attribute in

its own bid price equation and

the expected positive or zero

coefficient for each attribute in

its own offer function.

—An examination of cross

price relationships among the

attributes revealed an intriguing

and generally logical pattern of

interactions both on the demand

and the supply sides.

Palmquist

(1984),

.20297

Observation

About House

Selling,

Linear,

Semi-

Logarithmic,

Log-Linear

and Inverse

Semi-

Logarithmic

Functional

Forms

Dependent Variable: House selling price

Independent Variables:(1) Area of lot in sq. ft., (2) Finished interior

area in sq. ft., (3) Finished interior area squared, (4) Number of

bathrooms, (5) Year of construction, (6) Number of stalls in garage,

(7) Number of stalls in carport, (8) 1 if garage is detached from

house, (9) 1 if there is underground wiring, (10) 1 if there is a

dishwasher, (11) 1 if there is a garbage disposal, (12) 1 if there is

central air conditioning, (13) 1 if there is wall air conditioning units,

(14) 1 if there is a ceiling fan, (15) 1 if the date of sale was 1976,

(16) Excellent condition, (17) Fair condition, (18) Poor condition,

(19) Brick or stone exterior finish, (20) 1 if there is a full basement,

(21) 1 if there is a partial besement, (22) 1 if there are one or more

fireplaces, (23) 1 if there is a swimming pool, (24) The annual

arithmetic mean of the particulate air pollution level, (25) The

median age of the residents of the census tract, (26) The median

family income of residents of the census tract, (27) The percentage

of workers in the census tract that has a blue collar job, (28) The

percentage of houses in the census tract that has changed ownership

within the last five years, (29) The percentage of the population of

the census tract that is classified as non-white, (30) The percentage

of the population of the census tract over 24 years old that has

graduated from high school, (31) The percentage of the structures in

the census tract with 1.00 or less persons per room, (32) The number

of work destinations within the census tract divided by the area of

the census tract, (33) Adjusted monthly housing expenditure, (34)

Hedonic price of sq. ft. of living space, (35) Hedonic price of

bathrooms, (36) Hedonic price of the percentage of the census tract

with high school degrees, (37) Hedonic price of racial homogeneity,

(38) Hedonic price of lot area, (39) Hedonic price of reduction in

age of house, (40) Age of the purchaser, (41) 1 if the purchaser is

single, (42) Number of dependents in the family making the

purchase, (43) 1 if the purchaser is black.

—First, linear hedonic

regression equations were

constructed and in the second

srtage estimated logarithmic

linear estimates for house

characteristics in the study. To

reduce the costs of estimation,

the search was restricted to the

four functional forms most

frequently used: linear, semi-

logarithmic, log-linear and

ınverse semi-logarithmic.

—With approximately 200

coefficients estimated, there are

only 17 with incorrect signs and

none of these are for the most

important variables. Hedonic

regression results showed that

variables (3), (8), (18), (24), (28)

and (29) affects house prices

negatively. First 32 variables

except variables (3), (8), (18),

(24), (28) and (29) affects house

prices positively and the

variables which were positively

affects house prices have

expected signs and magnitudes,

also they were statistically

significiant. In the second stage,

variables (33), (34) (42) and (43)

were more effective on the house

prices and these variables which

were statistically significiant

have positive coefficients.


58

Study,

Data and

Functional Form


Goodman

(1988),

2857

Observation

About House

Selling,

Box-Cox

Functional

Form


Independent Variables: (1) 1 if central air, o

otherwise, (2) 1 if heating breakdowns in past 90

days, 0 otherwise, (3) Number of full bathrooms,

(4) Number of bedrooms, (5) Number of utility

breakdowns in past 90 days, (6) Age of house in

year, (7) 1 if full cellar, 0 otherwise, (8) 1 if

electricity used for cooking, 0 otherwise, (9) 1 if

open holes or cracks, 0 otherwise, (10) 1 if

additional heating equipment used, 0 otherwise ,

(11) 1 if Steam heat, 0 otherwise, (12) 1 if gas

heat, 0 otherwise, (13) Rating neighborhood, 1

(best)…,4 (worst), (14) 1 if fuses blown in past

90 days, 0 otherwise, (15) Number of lavatories,

(16) Years residing in dwelling unit, (17)

Number of rooms without hot air ducts, (18) 1 if

plaster broken over 1 foot 2 , 0 otherwise, (19) 1

if Access to other rooms through bedroom, 0

otherwise, (20) 1 if sign of rats ın past 90 days, 0

otherwise, (21) Number of rooms, (22)

Logarithm of property tax rate, (23)

Garage/carport available for use, (24) Location

dummies, (25) City dummies, (26) 1 if passenger

elevator in building, 0 otherwise, (27) Number

of extra features included in rent, (27) Number

of stories in building, (28) 1 if heat included in

rent, 0 otherwise, (29) 1 if Single family

Structure, 0 otherwise.

—Hedonic price methods define price indices for

owner and renter housing and define value-rent

ratios for the investment components of the

housing purchase. Permanent income is estimated

for both owners and renters. Tenure choice is

estimated using the price, value-rent ratios,

permanent and transitory incomes, and

sociodemographic variables. Housing demand is

estimated for both owners and renters.

—The adjusted R2 is 0.6025 for the value

regressions, as opposed to 0.4585 for the rent

regressions. Abathroom adds 26 % to the house

value and 28.5 % to the apartment rent. An

additional room adds 7.3 % to the value and 6.0 %

to the rent. An owner (renter) unit loses About 0.53

% (0.28 %) of value (rent) per year.

— Neighborhood effects are considerably weaker

for renter units. A unit improvement in the quality

of neighborhood Structure leads to a 3.8 % rent

increase; for owner housing the percentage increase

is 7.5 %.

—There is significant regional variation in owner

housing prices, there is less variation in quality-

adjusted rents.

—Variables (2), (5), (13), (16), (17), (19) and (22)

were affects house value and rents negatively.

Meese

&

Wallace

(1991), Time

Series Data

For 2

Different

City For The

Years

Between

1970–1988,

Trans-Log

and Log-

Linear

Functional

Form



(1) Number of bathrooms, (2) Sq. Ft. Of floor

space (m2), (3) Number of total rooms, (4) Index

of house condition, (5) Federal mortgage, (6)

Multiple sales dummy variable, (7) Mortgage

assumability dummy, (8) Residential zoning

dummy, (9) Swimming pool dummy, (10)

Fireplace dummy, (11) Age of dwelling (years).

—In this paper advocating the use of

nonparametric regression techniques to construct

housing price indices.

—The analysis includes an examination of the

variation in the implicit price of house attributes

over time, diagnostic checks of the adequacy of the

fitted hedonics, and simulated confidence intervals

for the Fisher Ideal Price index.

—For Diedmont city variables (6) and (11) have

positive and negative coefficients respectively.

Variables (1), (2), (3), (4), (7), (9) and (10) have

positive signs and have positive effects on the

house selling prices. Moreover, variable (5) states

less expensive houses and have negative signs.

—For San Francisco city only variable (5), (7) and

(8) have negative signs.


59

Study,

Functional Form, Data


Can

&

Megbolugbe

(1997), 944

Observation

About House

Selling, Linear

Functional

Form

Dependent Variable: House selling

price

(1) Living area in sq. ft., (2)

Land area in sq. ft., (3) Age of

the structure, (4) Composite

neighborhood quality score

Neighborhood quality variables:

—Owner-occupancy rate

—Median household income

—Percentage of residents with

college education

—Percentage of households

paying at least 30 % of income

on monthly housing costs

—Median value of owner-

occupied housing

—Vacancy rate

—Median age of housing stock

—Percentage of detached signle-

family units

—Percentage of white-headed, balack-

headed and hispanic-headed

—Study illustrates the importance of spatial dependence

in both the specification and estimation of hedonic price

models. In this article, presenting the importance of

spatial dependence on the specification of a house price

function due to the presence of spatial spillover effects

in the operation of local housing markets. With the

spatial models which constructed in the article, it would

be possible to adjust the confidence intervals of the

metropolitan –level indices to reflact the localized

dependencies in the house price determination process.

—Models also achieve very reasonable estimates of

marginal prices for selected attributes. Spatial

dependence plays an important role in the house price

determination process.

— Variable (2) is not statistically significant in both 6

regression equations of study. Except variable (2), other

variables have a hidh significance levels and have

different effects on the house selling prices.

—This study rpresents an attempt to derive useful house

price indices from large data sets containing only

alimited number of variables.

- —The R2 value in the spatial hedonic expansion models

which have strong consequences was more than spatial

hedonic and traditional hedonic models.

Meese

&

Wallace

(1997), 27606

Observation

About House

Selling In Two

District Over

An 18 Years

Period,

Translog and

Log-Linear

Functional

Forms


price


(1) Number of bathrooms, (2) Number

of bedrooms, (3) Sq. ft. of lot size, (4)

Number of rooms, (5) House quality

index, (6) Age of structure.

—This article examines a number of hypotheses that

underpin the repeat-sales and hedonic approaches to the

construction of housing price indices, as well as the

practical problems associated with the implementation

od either approach.

—Study examines a hybrid procedure that combines

elements of both the repeat-sales and hedonic regression

techniques.

—In this article, documenting the shortcomings of

repeat-sales price indices when they are constructed on

municipality-level data sets. The indices suffer from

sample selection bias and nonconstancy of implicit

housing characteristic prices, and the yare quite sensitive

to small sample problems.

—The standart variance specification of repeat-sales

approaches appears to be inappropriate for data at the

municipality level.

—Repeat sales methods reject the assumption that

changing attribute prices over time.


60

Study,

Data and Functional

Form


Powe,

Garro,

Brunsdan,

Willis

(1997), 872

Observation

About

Mortgage,

Linear and

Box-Cox

Functional

Forms


price


(1) Woodland index (Log), (2)

Proportion of children (aged<18),

(3) Within large urban area (0-1),

(4) Flor area, (5) Detached house

(0-1), (6) Semi-detached house (0-

1), (7) Age of property, (8) Full

central heating, (9) 1990 purchase

(0-1), (10) Garage (0-1).

—A search across the parameters of the linear Box-Cox

functional form was undertaken to find the best fitting

specification for the model. This was achieved by taking the

natural log of both the dependent variable and the forest

index while leaving the remainder of variables

untransformed.

—With the exception of the woodland access index, the

model is of the log-linear form. The marginal or implicit

price of a given characteristic under this specification can be

estimated as the product of the regression coefficient and

mean house price For illustrative purposes the marginal

value of a unit increase in a characteristic with respect to

mean house price was estimated.

—The functional form of the relationship between house

price and the woodland access index is double-log, and the

marginal price for this specification was estimated using this

expression: Marginal price of woodland = (regression

coefficient/access index)* house price.

—As the functional relationship between house price and

the woodland access index was nonlinear, the marginal price

of woodland access at any time was not constant but rather

a function of the current level of access.

—The results showed that the coefficient of variables (2),

(3) and (7) have negative signs and these variables affects

the house prices negatively.

—Within the model all of the explanatory variables were

significant at the 95% per cent level, had the expected signs,

and a respectably high goodness of fit was achieved.

Yang

(2000), 226

Observation

For The 160

Location

District,

Linear, Log-

Linear and

Box-Cox

Functional

Forms

Dependent Variable: Asking price

of per square metre of gross

construction area


(1) Gross construction area of living

room, (2) Number of bedrooms, (3)

Number of bathrooms, (4) 1 if the

public facilities provided for the

household; 0 otherwise, (5)

Distance from central business

District (CBD), (6) 1 if apartment

located in the west; 0 otherwise, (7)

1 if apartment located in the north;

0 otherwise, (8) 1 if apartment

located in the south; 0 otherwise,

(9) Perceived construction risk.

—In the study, three different model specifications of the

hedonic equation are run. They are a linear specification, a

log linear specification and a Box-Cox transformation. The

maximum-likelihood Box-Cox calculation for dependent

varable indicates that = ± 0.25. As a result of the different

functional forms, the coefficients of the three specifications

cannot be compared directly. However, their signs and t-

statistics are consistent and the Box-Cox regression

coefficients are effectively the same, both in the linear and

log linear specifications.

— The results of linear specification showed that 64.4 per

cent of the variation observed in housing prices. Most of the

coefficients are significant at the 99 per cent level, with the

exception of variable (3) is significant at 90 per cent and

variable (2) has no significance.

— The results of another two hedonic equations for sub-

samples showed that the influence of most variables on

housing prices remain stable, except that the value of

variable (3) is significantly different for the two different

locations. The marginal price of public facilities were fairly

low in the results.

— The high tolerance value for each variable suggests a

limited amount of multicollinearity among the independent

variables. For sub-samples Chow-test (F = 0.625) is not

larger than the critical value (= 2.54), which shows that the

null hypothesis of statistically stable estimated parameters

cannot be rejected at the 95 per cent significance level.

House quality affects the house prices very significantly.

The most important preference suggests households are

willing to pay additional expenditures to protect themselves

from low construction quality.


61

Study,

Data and

Functional Form


Leishman

(2001), 1155

Observation

About New

House

Selling,

Linear

Functional

Form



(1) Second bedroom, (2) Third

bedroom, (3) Fourth bedroom, (4)

Flor are (square meters) (5)

Bungalow, (6) Detached, (7) Mid

terrace, (8) Garage, (9) Dining room,

(10) Second bathroom, (11) En-suite,

(12) Box room, (13) Utility room,

(14) Year dummies.

—Hedonic regression models are constructed and Chow tests

are performed in order to test the null hypothesis of product

homogeneity between house builders.

— In the study two regression model was constructed. The

first regression model is intended to explain and predict

variation in house prices within a given local area with

reference only to the attributes or physical characteristics of

houses sold and time. The second regression model includes

the factor scores derived from the principal component

analysis as explanatory variables.

—The results show that the hedonic regression model

explains more than 83% of the total variation in house prices.

Most of the variables entered into the equation are significant

at the 99% level and the majority of the estimated parameters

have the ‗correct‘ sign, that is, they are positive or negative

in keeping with a priori expectations.

—In the second regression model, 12 of the factor scores are

statistically significant at the 1% level. The statistically

insignificant factors are those associated only with the

quarterly dummies. The collinearity statistics now indicate

that there is no multicollinearity problem.

Üçdoğruk

(2001), 2718

Observations

From The

Face To Face

Interview

With Real

Estate

Agencies,

Log-Linear

Functional

Form



(1) Number of balkony, (2) Number

of elevator, (3) Number of flats in

aparment, (4) Dwelling size, (5)

Number of rooms, (6) Floor number

of dwelling, (7) Age of dwelling, (8)

Heating system, (9) Furnishing status

of room and saloon, (10) Bathroom

floor, (11) Window carpentry, (12)

Roof proofing, (13) Wallboard, (14)

Location of dwelling, (15) Building

of kitchen, (16) Satellite system, (17)

Cable, (18) Pressure tank, (19)

Parking place, (20) Venetian, (21)

Solar energy, (22) Caretaker, (23)

Whether housing located at the

garden or site.

—Hedonic Pricing model estimated by using the simple

ordinary least squares method, for the best model choice used

Wald-F statistics and ―from the general to the particular‖

approach which was suggested byHendry.

—In this study, it is established both general and restricted

model and study with restricted model is more available.

Furthermore, it is mentioned that the percentage changes of

each variables affects the house prices.

— When it is examined conclusions of restricted model,

variable (5) was statistically significant.

—Except variable (5), all of the coefficients estimation

belong to the other variables correspond theoritical

expectations and is statistically significant.

—Taking place the housing in site, in the garden and with

solar energy resulted as economically insignificant.

—İmprovements that occurs in housing characteristics have

been raising housing prices at different degrees; both housing

features and external factors (floorspace of housing, whether

it is in the site) significantly affects the price.


62

Study,

Data and

Functional Form


Bover

&

Velilla

(2002), House

Selling

Observations

For 10 Cities,

Linear

Functional

Form


price


(1) Log swimming pool, (2)

Garage included, (3) Air condition,

(4) Fitted kitchen, (5) Fitted +

equipped kitchen , (6) Garden, (7)

Swimming pool, (8) Sport

facilities, (9) Fitted cupboards,

(10) Utility space, (11) Year

dummies, (15) Location area, (16)

Flor area

—In this study, estimating a quality-adjusted price index for

new multiunit housing.

—Study proposes a new method that controls in a very general

way for unobserved housing characteristics that are a potential

source of bias in the standard hedonic equations. This is

achieved by relying on the within-site variation (both cross-

sectional and over time) that allows site-specific effects to be

estimated. Standard hedonic equations estimates as well.

Dataset is rich in observed characteristics but, nevertheless, the

quality-adjusted price evolution is quite different in some cases.

—The index with general site specific effects are stronger than

the standard hedonic index. The standard hedonic index and the

index with general site specific effects are economically and

statistically significant for all cities (except the outskirts of

Madrid) and for most periods. These discrepancies can be taken

as an indication of the presence of unobserved house

characteristics which are taken into account by site effect

indices but not by the standard hedonic indices.

Ogwang

&

Wang

(2002), 832

Observation

About House

Selling, Linear

Functional

Form


price


(1) Lot size (acres), (2) Number of

bedrooms, (3) Number of

bathrooms, (4) Number of other

rooms, (5) Number of Garage

spaces, (6) Number of carports, (7)

Number of fireplaces, (8) 1 if city

central, 0 otherwise, (9) 1 if city

west, 0 otherwise, (10) 1 if city

north, 0 otherwise, (11) 1 if city

south, 0 otherwise, (12) 1 if city

South-east, 0 otherwise, (13) 1 if

rural, 0 otherwise, (14) 1 if

basement, 0 otherwise, (15) 1 if

gas/electricity, 0 otherwise, (16)

1if forced air/hot water, 0

otherwise, (17) 1 if aliminium, 0

otherwise, (18) 1 if wood, 0

otherwise, (19) 1 if stucco, 0

otherwise, (20) 1 if vinyl, 0

otherwise, (21)1 if osid, 0

otherwise, (22) 1 if outside

basement entry, 0 otherwise.

—The OLS estimates of the parameters of the hedonic price

function (model (1)) are generally very similar to the LAD

estimates. The fits, as measured by the adjusted R2 values (OLS

R2=0.59, LAD R2=0.57), are also very similar and highly

acceptable considering that cross section data are used.

— The coefficients of all the independent variables except

variables (12), (15), (18) and (20) are significant at the 10

percent level or better. Variables (1), (2), (3), (5), (6) and (7) are

significant determinants of residential housing prices.

—Observations remain valid when heteroscedasticity-adjusted

standard errors are used instead of OLS standard errors.

—The coefficients of variable (18) and (20) are all negative and

statistically significant. The coefficients of variables (8), (9),

(10), (11), (12), (19) and (21) are all statistically insignificant.

Neither variable (15) nor variable (16) is a significant

determinant of residential housing prices. The coefficients of

variables (14) and (22) are all positive.


63

Study,

Data and

Functional Form


Wilhemsson

(2002), Cross-

Sectional Data

Thet Include

Only 318

Transactions,

Box-Cox and

Log-Linear

Functional

Forms


price


(1) Living area (Price), (2) Lot

size (Price), (3) Quality (Price), (4)

Quietness (Price), (5) Changes in

real economics, (6) Permanent

income, (7) Mortgage, (8) Family

size, (9) Household age.

— The main objective of this paper is to demonstrate how the

linear expenditure system approach can be used in the

estimation of housing attribute elasticities.

—Estimation of the hedonic price equation is conducted using

a Box–Cox transformation. Study have chosen to use four

specifications of the hedonic price equation that together will

provide four estimates of the implicit price. Model 1, the base

specification, is a log-linear specification using the whole

sample period; Model 2 is a Box–Cox specification using the

whole sample; Models 3 and 4 are based on a different

transformation of the hedonic price equation in each time

period.

—The implicit prices were estimated by a Box–Cox

transformed hedonic price equation. However, the robustness

or sensitivity of the estimates in the linear expenditure system

was tested for different choices of specifications; the

conclusion was reached that they are relatively insensitive to

functional form.

All the estimated parameters differ statistically significantly

from zero and have the expected sign. Furthermore, each is of

reasonable magnitude. An increase by 1% of the living-area

attribute will increase the price of the house by 0.5%. The five

variables can explain around 60% of the deflated price

variation.

Toda&

Nozdrina

(2004) , 5282

Observation

About House

Selling

(February

2002) and

6551

Observation

About House

Selling

(April 2002),

Linear

Functional

Form


price


(1) Size of an apartment including

a bathroom and hallway, (2) The

size of a kitchen, (3) The distance

from a nearby metro station is

measured in meters, (4) The

distance from the city center, (5)

Location, (6) Wage arrears, (7) 2

room, (8) 3 room, (9) 4 and more

rooms, (10) Aroom can be

accessed dirctly from a hallway or

not (11) The material for he

exterior wall, (12) The number of

balkony, (13) Elevator, (14) The

materials for floor, (15) Apartment

repaired or not, (16) Location

condition, (17) East, (18)

Northeast, (19) Northwest, (20)

West, (21) Southwest, (22) South,

(23) Southeast with North as the

base, (24) The number of workers

the enterprises want to employ,

(25) Apartment new, (26)

Apartment under construction

—The data used in these estimations are the various attributes

of individual apartments and the prices proposed by the

agents who were selling them in February and April 2002. All

regression equations for two period were estimated by

ordinary least squares method and obtained the same results

for each one.

—The result of the estimation of regression equation on the

data in February 2002 is in the following. Number of

observations is 5,282. The F-ratio with the degrees of

freedom 22 and 5259 is equal to 256.58. The R-squared and

the adjusted R-squared are equal to 0.5177, 0.5157,

respectively.

—The result of the estimation of regression equation on the

data in April 2002 is in the following. Number of

observations is 6,551. The F-ratio with the degrees of

freedom 21 and 6529 is equal to 326.24. The R-squared and

the adjusted R-squared are equal to 0.5120, 0.5105,

respectively.

— Among the attributes of an apartment, the total size, the

size of kitchen, the number of room, the degree of certainty

with which a buyer can move into an apartment in a short

period play a significant role. The building materials for wall

and for floor, whether an apartment has been repaired,

whether the building has an elevator are also relevant in the

determination of apartment price.


64

Maurer&Pitzer

&Sebastian

(2004) ,

223.705 Total

and 84.686

Restricted

Observation

About House

Selling ,

Box-Cox

Functional

Form


price


(1) Dwelling area , (2) Elevator,

(3) Bathroom, (4) The number of

kitchen, (5) Number of garage, (6)

Garden, (7) Terrace, (8) New

dwelling, (9) Occupied by buyer,

(10) Partly occupied, (11)

Occupied by tenant, (12)

Basement, (13) Second floor, (14)

Thirth floor, (15) Fourth floor,

(16) Fifth floor, (17) Sixth floor,

(18) Seventh floor, (19)

Construction period dummies.

—In the study, for monthly and quarterly period 2 different

regression equation constructed. In the model which was

constructed for quarterly period, R2 value was found % 89.1

—Sign and size of the regression coe_cients are economically

intuitively plausible with except for the variable (12). The

coefficients for all other floors are positive and increase up to

the fifth floor. This means, that as the floor location of the

building increases, so does the price of the property. Similar

results were obtained for the construction year. The negative

coefficients show that in the case of occupancy of the

property, significant price reductions can be expected.

— Almost all parameters are significant at the 1 %-level.

— The White Heteroskedasticity Test statistic is significant

with W = 4984:274. Furthermore, was finding significant

autocorrelation in the residuals: The first (second, third) order

autocorrelation of the residuals: 0.213 (0.150, 0.113).

Therefore, following Newey and West's (1987) suggestion, t-

valueshave been calculated using heteroskedasticity and

autocorrelation consistent covariances even if only small

changes occur due to the large sample.

Wen&Lu&Lin

(2004) , 2473

Observation

About House

Selling for 5

District,

Linear

Functional

Form


price


(1) Total floor acreage of one

housing, (2) Housing age, (3)

Location (North-South, West-East),

(4) Dekoration Degree, (5) Number

of floor, (6) Garage, (7) Attic, (8)

Environment quality, (9) İner

environment quality, (10)

Community mangement, (11)

Nearby University life, (12) Life

establishment, (13) Education

establishment, (14) Entertainment

and physical education facility, (15)

Distance to the central business

district, (16) Distance to the west

lake (17) Traffic condition, (18)

Transaction time.

—In this sudy, establishes a housing hedonic price model,

gets implicit price of 18 housing characteristics, and sets the

evaluation model of the housing value as a result. Variables

(2), (3), (11), (12), (15) and (16) have negative effects on

house prices, other 12 variables affects house prices

positively.

— OLS method was used to estimate parameters and get the

regression equation. R2 of the model is 0.852, adjusted R2 is

0.851, D-W value is 1.991, all indicate that the fitness of the

model is high and possess good explaining capability. The F

value is 787.431 and the Sig. value is 0.000, indicate that the

fitness of samples datum with the model is meaningful in the

statistics and the regression equation is effective.

— Comparing with three traditional evaluation methods, the

new evaluation method, based on hedonic price, has three

advantages: 1) Hedonic price method extends from market

comparative method. Using hedonic price model could get the

implicit price of variables, and overcome the limitation of

correcting the differences of real estate by experience in

market comparative method. 2) Compared with income

capitalization method, it needn't determine the interest rate

and forecast the net income of the real estate. 3) Compared

with replacement cost method, the depreciation of real estate

can adopt the age of the housing as one characteristic variable,

and the hedonic price model can gel its implicit price directly.

Filho&Bin

(2005), 1000

Observation

About House

Selling, Linear

Functional

Form


price


(1) Number of bathrooms, (2)

Number of bedrooms, (3)

Dwelling area, (4) Land area, (5)

Dwelling age in 1994, (6) Distance

to nearest lake, (7) Distance to

nearest wetland, (8) Distance to

nearest improved park, (9)

Dwelling elevation, (10) Distance

to nearest industrial zone, (11)

Distance to the nearest commercial

zone, (12) Distance to the nearest

central business district, (13)

Dwelling age.

—Variables (3), (4) and (13) effects house prices more than

the other variables in the parametric model. Location

variables (7), (9), (11) and (12) have significant effects on

house selling prices in the parametric model. Variables (1),

(2), (3), (4), (6), (9) and (11) affects house prices positively,

variables (7) and (10) affects house prices negatively,

variable (12) affects house prices stronger than the other

variables. Variable (8) affects house prices less than the other

variables, variable (13) have no effects on house prices in the

nonparametric model.

—Nonparametric model liked much more than parametric

model by reason of the results obtained.


65

Cohen

&

Coughlin

(2005) , 2370

Total and 1643

Restricted

Observation

About House

Selling Near

The Airport,

Logarithmic,

Semi-

Logarithmic

and Linear

Functional

Forms

Dependent Variable: House sale

price deflated by median housing

price index for Atlanta


(1)The number of houses within

the 65 decibel day-night sound

level, (2) The number of houses

within the 70 decibel day-night

sound level, (3) 3 bedrooms, (4) 4

bedrooms, (5) 4 and more

bedrooms (6) 2 batrooms, (7) 3

bathrooms, (8) 3 and more

bathrooms (9) 2 and more

fireplaces, (10) Houses with more

than one story, (11) Age of house,

(12) Lot size in acres, (13)

Distance in miles from house to

airport, (14) A house was sold

occupied by blacks, (15) City

dummies, (16) Houses within the

65 decibel day-night sound level

noise contour for both 1995 and

2003, (17) Houses within the 70

decibel day-night sound level

noise contour for both 1995 and

2003, (18) Houses sold during

2000–2002 , (19) Houses sold

during 2000–2002 within the 65

decibel noise contour, (20)

Houses sold during 2000–2002

within the 65 decibel noise.

—This study using hedonic models for analyze the effects of

noise and proximity on housing prices in neighborhoods near

International Airport during 1995–2002. And addressing

complications caused by changes over time in the levels and

geographic distribution of noise and by the fact that noise

levels are measured only at the beginning and after the end of

the sample period.

—Hedonic regressions constructed for 1995–1999 and 2000–

2002 period in the study.

—Point estimates of the noise discount are sensitive to both

the functional form and the noise contours used to classify

houses in the study. proximity to the airport is related

positively to housing prices. Moreover, when this variable is

excluded, the estimated impact of noise on housing prices is

much less in absolute value than when this variable is

included.

—Generally speaking, housing prices were affected positively

by declining noise levels. After accounting for proximity,

house characteristics, and demographic variables, houses in

noisier areas sold for less than houses subjected to less noise.

—Proximity to the airport is related positively to housing

prices.

Li&Prud‘Hom

me&

Yu

(2006), 33.595

Observation

About Resale

House, Linear,

Semi-

Logarithmic,

Log-Linear and

Box-Cox

Functional

Forms


price


(1) Total square footage of living

area in the unit, (2) Total square

footage of lot area, (3) Number of

bedrooms, (4) Number of

bathrooms, (5) Number of

Garages, (6) Number of fireplaces,

(7) Number of total appliances, (8)

Age of a unit, (9) Age of a unit2

(10) Exterior finish is brick, (11)

New house, (12) Unit has

hardwood, (13) Heating fuel is

naturel gas, (14) Unit is at corner,

(15) Unit is at cul-de-sac, (16)

Terrace, (17) Distance to the

shopping center, (18)

Central/Built-in vacuum (19)

İndoor or outdoor pool, (20)

Whirlbath, (21) Sauna, (22) Air

condition system, (23) Unit is

located at downtown, (24) South,

(25) East, (26) WestDoğu, (27)

West then farwest, (28) Unit is

located at iner suburb.

— The Chow test results indicate that structural changes

between adjacent years are mild though statistically

significant.

—The pooled regression for the semi-log model, however,

results in a price index that closed matched those from

separate regressions on the annual base.

—In fact the hedonic price indexes are insensitive to structural

changes over the years and to the differences in the Laspeyres

and Paasche types formulation.

—The Box-Cox analysis rejects the linear, semilog, and log-

linear functional forms. It also suggests that the problem of

heteroskedasticity can be mitigated by choosing the more

correct functional form.


66

Yankaya,

Çelik

(2005) , 360

Observations

From The

Face To Face

Interview With

Real Estate

Agencies,

Linear and

Log-Linear

Functional

Forms


price


(1) Distance to the Metro

Station, (2) Distance to the bus

station, (3) Dwelling size, (4) Age

of dwelling, (5) Floor which

dwelling is located, (6) Dwelling

is located at the corner, (7) Central

heat, (8) Dwelling quality.

—In this study, it is used the cross-sectional hedonic price

model which is a special form of the multiple regression and

econometric modelling.

—In this study, linear and log-linear functional forms for 3

different areas, coefficient scores of variables resulted as

expected; most of the accounted parameters are significant at

95 % confidence interval.

— R2 value was calculated according to linear and log-linear

model resulted between 0.70 and 0.75, in both models the

most important determining of value is size of housing.

—Variables (1), (2) and (4) affects house prices negatively

and the effects of other variables on house prices is positive.

—Conclusions of model points out that investment in access

infrastucture increases hose prices in domain.

Hai-Zhen

&

Sheng-Hua

&

Xiao-Yu

(2005) , 2473

Observation

About House

Selling ,

Linear

Functional

Form


price


(1) Floor area, (2) Housing age,

(3) Orientation (North-South, East-

West), (4) Decoration degree, (5)

Number of storeys, (6) Garage, (7)

Attic, (8) Environment quality, (9)

Inner environment, (10)

Community management, (11)

University nearby, (12) Life

establishment, (13) Education

establishment, (14) Entertainment

facility, (15) Distance to the

central business district, (16)

Distance to west lake, (17) Traffic

condition, (18) Transaction time.

— R square of the model was 0.852, adjusted R2 was 0.851,

the D-W (Durbin-Watson) value was 1.991, all which

indicated the fitness of the model was high. The F value was

787.431 and the p-value was 0.000, which indicated that the

fitness of samples data to the model was meaningful

statistically and that the regression equation was effective.

— The significance level of t test of most coefficients was

smaller than 10%, which indicated the corresponding

coefficient had significance influence. At the significance

level of 10%, 14 independent variables entered the model, and

the significance levels of 12 variables among them were

smaller than 1%. The coefficients of the variables (2), (3),

(12) and (13) are insignificant and these variables does not

affect the house prices.

—Variables(11), (15) and (16) affects house prices negatively

and also the other variables affects house prices positively.

— For the standard housing, the contribution rate to the

housing price of architecture characteristic was 60.0%, the

contribution rate of neighborhood characteristic was 16.5%,

the contribution rate of location characteristic was 19.8% and

the contribution rate of other characteristic was 2.7%.

estimating hedonic demand parameters in real estate market

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