Flood Risk &House Price

8/3/2019 Flood Risk &House Price

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Joumai of Real Estate Finance and Economics, 4: 395-407 (1991) 1991 KJuwer Academic Publishers

Housing Prices and Flood Risk: An ExaminationUsing Spline RegressionJANET FURMAN SPEYRERAssistant Professor of Economics, Department of Economics and finance. University of New Orleans,New Orleans, LA 70148WADE R. RAGASProfessor of Finance, Department of Economics and Finance, University cfNew Orleans, New Orletms, LA 70148

AbstractThis article e:uniines the impact of flood risk and nxandatory flood insurance on property values. Using a largedata set of almost 2,000 hom es sold in the New Orleans , Lou isiana, a rea from 197] through 1986, the analysisconfirms the finding of earlier studies that location in a floodplain does reduce property values. The presentstudy, using spline variables to adjust for locational variation in the data and an improved measure of insurancecost, reveals that much of this reduction can be attributed to mandatory flood insurance coverage. Moreover,while unexpected flooding does increase the insurance cost capitalization, repeated flooding does not seem toreduce property values further.Key words: Flood risk. Flood insurance, Spline r^ression

Reported annual flood damage from urban flooding often exceeds hundreds of millionsof dollars in the United States. In addition., flood control structures, drainage systems, andflood insurance subsidies cost billions. Not included in these costs is the reduction in pro-perty value experienced by owners in areas perceived to be flood prone. Three questionsabout urban rain-runoff, flood-induced reduction in value are empirically examined in thisstudy. First, within areas with extensive flood insurance coverage and recurring actual floodrisk, are property values significantly lower? Second, do differences in insurance cost ex-plain property value reduction in flood-prone areas? Finally, does recurring urban rain-runoff flooding change the magnitude of the adverse effect on property values?The first two questions have been examined in the literature using cross-sectional analysis.Two prior articles analyze the impact of floodplain location on property values. Shilling,Benjamin, and Sirmans (1989) review the specifics of the National Flood Insurance Pro-gram and its redistributive effects on owners of different types of property. The authorsstudy 114 transactions in Baton Rouge, Louisiana, durin g a 15-month perio d and find asignificant impact of floodplain location ($4,500 for the average-valued home). They usethe actual flood insurance premium to investigate the effect of higher insurance costs onproperty values and conclude that flood insurance cost is capitalized into house value ata discount rate of 4 percent.However, since flood insurance cost is the insurance rate timeshouse price, the correlation between price and insurance cost m ^ make it difficult to


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3 96 JANET FURMAN SPEYRER AND WADE R. RAGAS

interpret the regression coefficient of flood insurance cost. The dummy variable approachused by the authors to estimate the effect of flood insurance on property values yields con-sistent results with their insurance cost fmdings. MacDonald, Murdoch, and White (1987)develop a theoretical model of willingness to pay to avoid flood risk. Using 217 observa-tions scattered over Monroe, Louisiana, during a three-month period, they find a similareffect on house value. Locations in a flood-risk zone experience capitalization even whenflood insurance coverage exists. The capitalization rate they fmd for flood insurance costis slightly less than 3 percent.

Skantz and Strickland (1987) studied the impact of extensive urban flooding on 133 homesin Houston, Texas, over a four-year period in search of an answer to the third question.Based on the sales of only 36 houses after flooding occurred, they conclude that an in-crease in insurance rates caused a significant decline in property values, but that the actualfiooding incidents did not affect value.We address these three questions using a unique time-series data base of about 2,000sales drawn from the metropolitan area with the highest flood claims in the past decade-New O rleans. Two neighborhoods of differing age and character (urban versus suburban)were studied. We also introduce spline variables into our regression equations which makeit possible to isolate the impact of fiood risk from other locational variables. The cost of

mandatory flood insurance enters as a rate index in the analysis to limit the correlationbetween flood insurance prem iums and house value when trying to determine the capitaliza-tion of insurance cost. Tests for changes in flood insurance capitalization are conductedafter serious flooding incidents. These improvements in data and methodology enable usbetter to estimate the cost of flood risk and mandatory flood insurance and therefore todetermine the benefits of fiood control efforts.

1. DataThis research uses a data file created from appraisal reports on single-family houses sup-plied l^ the majority of active appraisers in the New Orleans area to tbe Real Estate MarketData Center of the University of New Orleans. Descriptive housing characteristics havebeen maintained from these records since 1971. Actual transaction prices, a matter of publicrecord, are verified on eacb appraisal and retained on the record. Regular use and reviewby practicing appraisers further insures the accuracy of the data. The data file is more com-plete than the Multiple Listing Service records typically used in this type of study.Significant u rban flood ing due to unusual rainfalls (8 inches to 10 inches in less than24 bours) occurred in 1978, 1980, and 1983 in the New Orleans area. A review of allnewspaper accoun ts of these incidents identified several geographic a reas with obvious floodrisk. Our study areas include two groups of neighborhoods with recurring media atten-tion. A suburban area in Jefferson Parish encompassing three square miles with Al, B,and C fiood risk ratings and a more centrally located (urban) area in Orleans Parish ofabout two square miles from Al, A8, and B fiood risk ratings were selected.'

From lowest to highest fiood risk, Federal Emergency Management Agency (FEMA)assigns categories ranging from C (virtually no flood risk), B, Al, and A8 to property inthese areas. Technically, FEMA assigns C to areas outside of a 500-year flood (< .2 percent


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HOUSING PRICES AND FLOOD RISK; AN EXAMINATION USING SPUNE REGRESSION 397

probabil i ty off looding n a given yea r) , B to areas be tween l imits of 100-year and 500-yea rfloods ( .2 to 1 percent probabi l i ty o f flood in a given year), and A to those areas subjectt o 1 00 - y e a r fl o od ( > 1 p e r c e n t c h anc e o f f lo od ing in a gi v en y e a r ) . Wi t h in t h e A zone ,f lood haza rd inc rea ses f rom Al to A30 , wi th a h ighe r suff ix indica t ing h ighe r floo d r i sk .^FEMA da t a r e v e a l t h e ex t en t o f fl o od dam age i n t h e s e tw o p a r i s h e s i n t a b l e I . In Or l e ansPar i sh be tw een 1977 and 1986, the re w ere 17,552 c la im s to ta l ing $75 .4 m i l l ion . In Jeffe r son,2 1 ,867 c l a im s t o t a led $121.7 m i l li on ov e r t h e s am e 10 -y e ar p e r i od . Yea rs o f m a s s i v e s t r e e tf looding have infl ic t ed lo s se s ave raging $4,300 to $5 ,600 pe r FEMA c la im in these two a rea s .Tab le 2 s um m a r i ze s s e l e c t ed c h a r a c t e r i s t ic s o f t h e da t a in e a ch o f th e s t udy a r e a s . Means ,s t anda rd dev i a t i ons , and r ange s o f s e l e c t ed v a r i a b l e s i n e a ch o f t h e s e two ne ighbo r hoodsa r e l i s t ed .Table L Flood insurance c l a im s : Or leans* and Jeffe rson** Par i sh es , 1977-1986 .

Year1977197819791980198119821983198419851986

To t a l1977-1986

C l a i m s3

313310663099671342541552 67517 , 552

Or l e an s P a r i s hDol l a r s

$ 4 ,41011 ,783 .421228 .70527 .702 . 8612 , 9 2 2 , 0 2 43,447,34826 .544 .734

7,2062 . 7 29 , 16615.784$75,385,659

Ave r ageCla im$ 1,470

3,7612 . 1 5 84.3913.0222 , 5694 ,9021,4411 0 . 2 2 23 , 157

$4,295

Cl a ims19

4 0 0 0120794032 738193514712 0 5 2521 ,867

Jeffe rson Par i shDo l l a r s

J 48.73618,443,478271 , 7874 1 , 2 7 0 , 5 3 27 1 1 . 5 9 817,769.79815.493 .2321 , 11727 ,680,446

9.620

$121 , 7 00 , 344

A v e r a g eCla im$ 2 , 565

4 ,6112 , 2 6 55 , 1982 , 1764 ,6534 ,4091613,4891,924$5 , 565

Source: Mr. Wayne F i r l e y . Reg i ona l Admin i s t r a t o r f o r F ede r a l Eme rgency Managemen t Agency .Or l e an s P a r i s h : 55 , 8 52 po l i c i e s ; $3 , 766 ,972 , 500 i n s u r ed ; $3 , 037 , 574 p r em i um f o r 1985 ." J e f f e r s on P a r i s h : 51 . 233 po l i c i e s ; $3 , 864 ,444 ,600 i n s u r ed ; $ 11 , 9 23 , 500 p r em i um f o r 1985 .Table 2. S e l e c t ed v a r i a b l e me an s , s t a nda rd d e v i a t i on s , a nd r ange s .

Vt r i s b t e NamePriceLiving arc* (sq, f t . )Flood AlFlood A8Insurance ra le indexCentra l ai rNumbe r of f i r ep l a c e sLot fronlage (ft.)Off-siieel parldngConditionHalf bathsCentra l heatPiersI n su r an ce amoun l t

Mean73090.472183.170 ,91

81 .130.980 . 1462 . 060,925 .940.360 .9990 . 04148.15

Su rbu r t a n*StandardDeviat ion37967.73656.790 . 28

33.420 , 1 30,3510 , 070 . 280.640.680 , 2 90 , 1 975.91

Minimum10400

97 00

000

300300019,50

Maximum27580054601177,25121 1 5175

11997.50

Mean66961,452429.610 . 260 , 0831 , 130 ,31O, l t42 ,100.383,130 . 290.390,92149,64

Urban**StandardDeviat ion50508 ,941112 , 250,440 . 2 746,410,460,3814,S20,490 ,830 ,610,490 2 779 59

Minimum1 0 0 0 08070

0000

2 00100019,69

Maximum30000069321

1156.0912

1 5017511430 .00

No, of obse r va t i on s " 1 ,2 29, ' ' N o . o f obse r va t i on s = 769, tFo r p r ope r t i e s in FEMA tloo dzone A on ly .


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39 8 JA I^T FURMAN SPEYRER AND WADE R . RAGAS

2 . M o d e lThe question of whether buyers pay a premium for homes in areas with less flood riskis theoretically represented by a bid function similar to that used by W heaton (1977). A ssumeV(Fi) is the maximum utility that can be achieved with flood risk i. Then, the bid forreduction in flood risk from Fj to Fj is the gap between the minimum expenditurenecessary to achieve V(Fj) with F, and the minimum expenditure necessary to achieveV(Fi) in neighborhoods with greater flood risk Fj . The bid function 6iFi, Fj) is the max-imum amount that the consumer would pay to obtain flood risk F, rather than Fj .

The housing market does not explicitly reveal the bids for different flood risk zones.Rather, the transactions prices of homes result from the interaction of supply and demandforces for many physical and locational characteristics in addition to flood attributes. Thehedonic price approach is used to isolate empirically the impact of flood risk in house value.At any time, house value, H, can be represented by the reduced form equation;

H = f(P, L, F), (1 )where: F is a vector of physical characteristics of the home,

L is a vector of locational characteristics, andF is the risk of flooding from urban rain runoff.For the calculation of an bedonic price index to examine tbe value of flood risk reduc-tion, the vector P includes lot frontage, living area, number of half-baths, cond ition, num berof fireplaces, dummies representing off-street parking, elevation on piers, and central heatingand cooling systems. Locational variables capmring urban amenities and disamenities anda measure of access to an employment center are included in L?In order to examine the impact of actual flood occurrences on house value, house salesfrom 1971 to 1986 are included in the sample. Introduction of significant variation in timeinto the cross-sectional analysis requires knowledge of the functional relationship betweentime and value. Dumm y variables for each year from 1972-1986 are included in the regres-sion to correct for intertemporal variation in price. The nonsimultaneity problems usingtime-series data are mitigated but not resolved by tbese year dummies. Several altemativespecifications were tried. Other functional forms (linear, log) for time using an otherwisesimilar bedonic price calculation were attempted. The nonmonotonic nature of the rateof change in price over a 16-year period was best represented by the specification usedhere. Another altemative, deflating all house prices by the consumer price index, is notsatisfactory because of the rapid increases and decreases in property value due to fluctua-tions in the local econom y's oil and gas industry. T bese price chang es sharply differ fromthe national price pattem. Shorter time-series within tbe sample were tested for differencesbetween the partial- and full-sample regressions to ascertain the stability of tbe observedcoefficients.For the first set of regressions, flood risk F, is represented by dummy variables representingthe most risky F EM A flood zone in each of the two geographic areas. In the urban area,a dummy variable for flood zone with highest risk, AS, is included in tbe regression to

compare the property values in these areas to those of flood zones Al and B, the less riskyflood zones in the area. In the suburban area, a dummy variable representing the most


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HOUSING PRICES AND FLOOD RISK: AN EXAMINATION USING SPLINE REGRESSION 399

risky FEMA flood zone in the area., Al, is used to compare property values in these areasto those in flood zones B and C.In the second set of analyses, a flood insuran ce cost index is used to explain the influenceof higher flood insurance premiums on house value. This flood insurance cost index com-pares the cost of insurance for each of the properties to the average effective rate for in-surance in the 1971-1974 period . T hese m andated insuranc e costs are calculated from FEM Arates and coverage limits. The index is constructed as follows:Insurance _ [" unit insurance premium /unit sales price 1 -^ .Rate index 1 average premium 71-74 /avera ge sales price 71-7 4 J

3 . Empirical findingsHomes in the two areas were first examined using linear and semi-log specifications ofthe mode l. Then , Box-Cox transformations of the regression equa tion were tested. Casseland Mendelsohn (1985) suggest that when many of the independent variables are not con-tinuous, Box-Cox transformation of independent va riables may be misleading. Because m orethan half of the variables in the regressions are not continuous, transformation of only thedependent variable is reasonable. Box-Cox transformation of the dependetit variable onlyreveals X of .31 in the suburba n are a and X of .13 in the urba n area . (X = 1 indicates alinear regression; X = 0, a semi-log regression.) Box-Cox findings are more difficult forthe reader to interpret than are the linear and semi-log forms. Because the signs and sig-nificance of the variables of interest in the Box-Cox regression are not different than thoseof the semi-log regression, w e have chosen not to report the Box-Cox regression coefficients.

The first set of linear and semi-log regressions tests whether location in zones with floodrisk reduces property values. These findings are reported in table 3.In the suburban n eighborhood, location in flood-prone neighborhoods does diminish prop-erty values. The reduction in the Al FEMA zone (the riskiest in that area) is about $6,100in the linear regression. The semi-log form of the regression reveals that location in theAl zone leads to a 4.2 percent reduction in property values.The urban neighborhood analysis reveals property value reduction for the highest floodrisk zone (A8) of about $13,7(X) in the linear regression. The finding that location in theA8 zone reduces property values by 6.3 percent in the semi-log form is not significant atthe 10 percent level. One possible explanation for the insignificance of this flood variablein the semi-log form is that the A8 specification of flood risk based on ground surfeceelevation may not be entirely indicative of likely flooding for these homes. In the studyarea, significant flooding has occurred in the less risky FEMA flood zones. In addition,the homes in the urban area are much older than the suburban homes. The owners havetherefore experienced many substantial rainfalls and may not have suffered much damage.Finally, further analysis to adjust for locational va riables other than flood risk m ^ be neededto separate the impact of flood risk from that of other characteristics that are highly cor-related with price.Before adding the ge ographic variables, w e note that many of the other variables designedto explain differences in home values in this first set of regressions have the expected


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400 JANET FURMAN SPEYRER AND WADE R. RAGAS

Table 3. Effect of flood risk zone on property value.

Sample SizeAdjusted R-SquareDurbin-WatsonIntercept

Condition

Central ai r

Central heat

Distance fromemployment centerProximity to lake

Frontage

Half-baths

Living area

Number of fireplaces

Off-street parkingPloodzone A l

Floodzone A8

Year 1972

Year 1973

Year 1974

Year 1975

SuburbanLinear1229

0.79691.680

-49288.1**(-6.089)4514.52**

(5.172)2650.69

(0.696)

-105.68*(-2.096)7635.19**

(4.684)10683*(1.864)2910.91**(3.622)25.00**

(25.839)12798.52*-

(7.011)4265.86(1.488)4285.99*

(2.359)-6124.02***

(-3.367)

3285.17(1.230)

9016.09**(3.404)10086.19**(3.818)15523.44**(5.988)

Semi-Log1229

0.78941.7779.0485**(86.514)0.0733**

(6.501)0.2044**

(4.156)

-0.0008(-1.191)0.0771**(3.661)0.0014*

(1.841)0.0308**(2.970)0.0003**

(26.852)0.0179

(0.758)0.0650*

(1.754)0.1058**

(4.509)-0 .0415*(-1.764)

0.0828*(2.401)0.1652**

(4.826)0 .2201 (6.448)0.2823**(8.428)

UrbanLinear769

0.72621.791

-57105.9**(-5.818)6244.83**(4.521)17566.89**

(4.713)8850.63*

(2.431)-377.99**(-3.969)

729 .80 '*(8.805)11804.03**

(6.869)9.95**

(9.061)12486.74**

(4.280)870.27

(0.234)-379.50(-0.176)

-13691.7**(-3.590)

2899.93(0.498)

615.86(0.109)

6395.14(1.124)5306.71(0.916)

Semi-Log769

0.80781.7238.5068**

(68.654)0.1704**

(9.775)0.1817**

(3.862)0.1705**

(3.710)-0 .0073**(-6.748)

0.0091**(8.690)0.1162**

(5.356)0.0002**

(15.473)0.0686*(1.862)0.0330

(0.703)0.0583*(2.143)

-0.0634(-1.317)0.0733

(0.997)0.0070

(0.099)0.0793

(1.104)0.0787(1.076)


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HOUSING PRICES AND FLOOD RISK: AN EXAMINATION USING SPLINE REGRESSION 40 1

Year 1976

Year 1977

Year 1978

Year 1979

Year 1980

Year 1981

Year 1982

Year 1983

Year 1984

Year 1985

Year 1986

Quarter 2

Quarter 3

Quarter 4

19377.94**(7.519)24135.73**

(8.552)42545.69**

(16.209)48016.23**

(17.927)54889.68**

(14.656)44418.27**

(4.371)58754.64**

(12.413)66817.42**(21.537)76288.02**

(24.229)64967.07**

(18.253)76665.38**

(22.014)1092.43(0.740)876.82

(0.606)4767.65**

(2.965)

0.3545**(10.646)0.4201**

(11.521)0.6766**

(19.951)0.8007**

(23.136)0.8836**

(18.259)0.8147**

(6.205)0.9733**

(15.915)0.9788**(24.417)1.0870**

(26.720)0.9084**

(19.753)1.0679**

(23.733)0.0033

(0.174)0.0228

(1.220)0.0588**

(2.828)

20201.56**(3.542)28965.75**

(5,076)41862.62**

(6,802)43502.94**

(7,009)50222.83**

(6.570)65546.12**

(4.061)62891.94**

(6.154)70086.71**

(11.146)72547.31**

(11.742)89304.29**

(11.719)74275.42**

(10.608)2774.02

(0.981)3957.94

(1.395)5205.47*

(1.733)

0.3020**(4.194)0.5290**

(7.344)0.6900**

(8.882)0.8038**

(10.260)0.9402**

(9,744)1.1578**

(5.682)1.0829**

(8.394)1,1202**

(14.112)M 4 6 5 * *

(14.701)I.3050**(13.566)1.1265**

(12.745)0.0520

(1,456)0,0561

(1.566)0.0678*(1.787)

*denotes significance at 10 percent level.**denotes significance at 1 percent le vel.Note: t-statistics in parentheses.signs and are significant. The annual dummy variables reveal a general increase in prop-erty values (including the impact of inflation) through 1980. The effect of these time variableson value is less even in the post-1980 period due to the general recession in the UnitedStates and the decline in the New Orleans economy relative to that of the United Statesfollowing 1983. The average rate of price inc rease p er year from 1971 to 1986 is about 5percent in both urban and suburban areas.

As expected, distances from major centers of employment have negative coefficients inboth areas. Moreover, the gradient is flatter (.08 percent per hundred block) in the suburbsthan in the urban neighborhood (.73 percent per hundred block). This result is consistentwith an exponential decline in property values from the central business district (CBD),because the urban area is about seven miles closer than the suburban area to the CBD.


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4 0 2 J A N ET F U R M A N S P E Y RE R A N D W A DE R . RAGAS

For the suburban neighborhood, all of the physical characteristics with significant coef-ficients have the expected signs. In the urban neighborhood, only off-street parking hasan unexpected sign in the linear specification, but the coefftcient is insignificant. In thepreferred semi-log regression, off-street parking has the expected positive sign and issignificant.

Because FE M A flood zones are assigned to broad geographic areas, the flood zone coef-ficients may be making some other important locational characteristics. In order to tninimizethis possibility, we assign to each address anx, y coordinate in a Cartesian plane and thenuse the technique called "Bezier Spline" or "Spline" curves to capture variables with purelylocational variation. Simply, the .x-axis of this Cartesian plane is divided into intervals ofequal length. A parame tric cubic curve is fit to the data on each interval so that the curvesfor each adjoining interval intersect at tbe knot points (the end points of each interval).Suits and associates (1978) define a spline function of third degree as:

y = [a, -^ b, (X - Xo) + C| iX ~ Xo)2 + d,iX - XQ)^ >I+ [a2+b2iX- X,) + C2 (X - Xi)^ +d2iX~ XO'] D^+ [613 + bsiX ~ Xi) + C3 (X - X2)2 + diiX ~ X2)'] D3 + U (3)

where D, = 1 if X,_i < X < X else >, = 0 , i = 1, 2 , 3.By requiring all segments in a direction to be of equal length, applying appropriate con-straints to insure continuity of the function and its first and second derivatives, and re-arranging terms, this equation reduces to a form amenable to regression formulation. Asdemonstrated t^ Suits and associates (1978) for a single vector direction, the equation isreduced to:K

Z = a i -H fci (X - Xo) + Cl (X - Xo) +diiX- X^)' + S (^.-M " ' .Jf " ^d ' A *'= ' (4 )

where K is the nutnber of knots (points equally spaced within the boundaries) and K+\intervals occur and D,* = 1 if X > X, and D,* = 0 otherwise.In the present regressions AT = 3 in the suburban area and A" = 4 in the urban area.The spline variables are defined for the second set of regressions as follows: Xj = X

Xo; X2 = (X - Xo)=; X3 = (X - Xo) ; X4 = (X - X,)^ O ,; X5 = (X - X;)^ D^; X^ =(X X3)' D3; and X7 = (X X4)' D4 (applicable only in urban area).The result is a polynomial made up of tangent segments each of which is a third orderpolynotnial. The model contains a linear, quadratic, and cubic parameter for the X variableand then a series of cubic representations that overlay only parts of the surface segments.Tbe regression coefficients for aj, 6], C|, and d] are directly observable while the valuesof ! may be calculated from these re gression coefficients. T he relative contribu tion ofa directed distance spline can be estimated using an F-test and partial R^ for the componentsof a regression (Suits et al., 1978). These estimates of changes in slope are beyond thescope of this article, but have been elaborated by Anderson (1982).The accepted strengths of splines as a fonn of nonlinear-equation approximation aresubstantial. S plines can provide the theoretically smoothest fit of nonlinear data (M cN eil


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HOUSING PRICES AND FLOOD RISK: AN EXAMINATION USING SPLINE REGRESSION 403

et al., 1977). The imposition of some constraints insures that first and second derivativesof tbe surfece are continuous and the tangent vectors are continuous (Foley and Van Dam,1982). The loss in degrees of fi-eedotn is less than that of a fourth or higher order trendsurface analysis (TSA) fit. Standard measures of significance for regression models aredirectly applicable (Suits et al., 1978). Finally, the spline model, because of its direct con-sistency with the specification of regression models, allows the introduction of other hedonicvariables into the model and a direct linkage with the existing literature (Suits et al., 1978).The splines model, however, does have its weaknesses. Extrapolations of the functionsbeyond the data range to which it is applied are not defined. Large gaps of missing dataor clumps of data can lead to misleading and inappropriate representations as encounteredwith TSA. Spatial autocorrelation may still exist even in well-specified models, and nostraightforward method of removing this remaining autocorrelation appears to exist (Anselin,1986; Anselin and Griffith, 1988; Hain ing, 1986). The splines model should still produceless spatially biased hedonic coefficients than models with no control for spatialautocorrelation.

An additional concern is that the exceptional fit of the splines model to the underlyingtrends in the data l^ small patches or segments of the area under study can pose seriousproblems for some research. If the location of an externality coincides with a patch (gridsection bounded by knot points) then measuring changes in a dependent variable due tothis location specific externality may not be feasible. For example, the impact of locationin a fiood zone may be obscured by the splines variables which accurately measure thisand any other locational variation in the data.The set of regression results including the spline locational externality variables and aninsurance index cost are reported in table 4. These equations focus on the impact of man-datory flood insurance cost rather tban on location in FEMA high-risk zones because ofthe problem described above using the splines model with locational dummy variables.Once a gain, virtually all the variables have the desired sign and significance. The insurancecost index is found to have a negative significant coefficient in all but the semi-log regre s-sion in the urban a rea. T he coefficient of the insuranc e cost index variable in the suburbanarea is .(X)18 in the semi-log model. A higher annual insurance cost of only $1.48 leadsto an apparent $109.20 decrease in property value wben the equation is evaluated at themeans. The implied discount rate is 1.36 percent per annum in perpetuity. Tbe linear subur-ban model results in a discount rate of 0.97 percent and the linear urban model yields animplied rate of 1.93 perce nt. A discount rate equal to a real rate of interest in the 3 percentto 4 percent range would be expected if all capitalization were due to insuranc e costs. Thelower value found in this analysis suggests that some of the observed capitalization maybe due to the actual incidence of flooding. A three-dimetisional graph of the data supportsthis hypothesis.

Figure 1 depicts the regression resu lts for the suburban area using splines and the in-surance index.' The three-dimensional graph reveals the substantial locational variationin price associated with major streets which can be a positive or negative externality. Prox-imity to lake views, a positive externality, is also visually evident. Two areas of actual bigbincidence of flood damage (based on inspection by the authors) do have substantially lowerproperty values per square foot than nearby areas. These two areas are prone to have im-passible streets when only five or six inches of rainfell occurs over period of a few hours


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40 4 JANET FURMAN SPEYRER AND WADE R. RAGAS

Table 4. Effect of flood insurance costs on property value including locationai adjustment.

Adjusted R-SquareDurbin-WatsonInterceptConditionCentral airCentral heatFrontageHalf-bathsLiving areaNumber of fireplacesPiersOff-street parkingInsurance cost indexXIX2X3X4X5X6X7Year 1972Year 1973Year 1974Year 1975Year 1976Year 1977

SuburbanLinear0,80621.657

-36429.9**(-4.174)4748.05**(5,476)3231.09(0,861)

89.09(1.447)2675.55**(3.405)

23.37*(23.762)12089.03**(6.688)41164.86(1.484)3904.74*(2.188)-182.76**(-8.903)110.76(0.075)105.68(0.373)-8 .75(-0.576)15,58(0.768)

-11.25(-1.176)8.03(L261)

2885,22(1,101)7654,43 (2,945)544.38(0,196)7598.03**(2,839)11527.93**(4,357)15726,64**(5,444)

Semi-Log0.79741.7739.2499**(81.676)0.0740**(6.577)0.2072**(4.257)0.0014*(1.801)0.0276**(2.711)0.0003**(24.728)0.0069(0.296)0.0650*(1.786)0.1018**(4.398)-0.0018**(-6.836)-0,0176(-0.915)0.0036(0.971)

-0.0002(-0.994)0.0003(1.023)

-0,0001(-1,033)0,0001(1.445)0.0767*(2.258)0.1468**(4.352)0.1222"*(3.390)0.2016**(5.805)0.2757"*(8.031)0.3341**(8.913)

UrbanLinear0.72841,778

-57573.6**(-5.816)6409.82**(4.638)15977.05**(4.296)9024.05*(2.486)637,83**(7.583)10868.83**(6.281)10.40**(9.590)12883,53**(4,393)-477,89(-0-128)-895,6(-0-413)- 77 , 33**(-3.104)-4809 ,15 -(-2-127)1392.08**(2.772)-81.94**(-2.909)112.56**(2.901)-49.27*(-2.336)51.25*(2.180)-153,42*(-1.892)

1109.79(0.190)555.92(0.098)4452,82(0.778)3744,67(0.640)18334.17**(3.188)26017.95**(4.491)

Semi-Log0.81351.7388.4625**(68.481)0.1711**(9.916)0.1640"(3.534)0.1756**(3.876)0.0085**(8.107)0.1058**(4.900)0.0002**(15.878)0.0801*

(2.187)0.0248(0,532)0.0535*(1.980)0.00029(0.928)-0.0820**(-2,906)0,0177**(2.819)- 0 . 0 0 0 9 "(-2.681)0,0012*(2.426)

-0-0004(-1.376)0.0005*(1.784)-0.0023*(-2.303)0.0547(0.751)0.0120(0.170)0.0776(1,086)0.0703(0,962)0.2949**(4,108)0-5133**(7.098)


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HOUSING PRICES AND FLOOD RISK: AN EXAMINATION USING SPLINE REGRESSION 405Year 1978Year 1979Year 1980Year !981Year 1982Year 1983Year 1984Year 1985Year 1986Quarter 2Quarter 3Quarter 4

34246.58**(12.462)39655.28**

(14.225)45755.27**(12.118)41799.89**(4.199)54981.30**(11.875)62893.13**(20.390)72252.68**(23.064)60787.91**(17.226)76799.56**(22.533)

1433.22(0.991)588.54

(0.416)4958.69**(3.150)

0.5920**(16.602)0.7152**

(19.773)0.7911**(16.147)0.7763**(6.010)0.9337**(15.542)0.9339**(23.335)1.0445**(25.697)0.8706**(19.015)1.0696**(24.185)0.0086(0.457)0.0194

(1.059)0.0601**(2.941)

41981.06**(6.803)41332.14**

(6.558)51297.93**(6.710)64445.46**(4.001)61436.99**(5.968)70833.24**(11.152)70172.87**(11.313)88395.78**(11.547)76228.27**(10.736)

3271.12(1.153)4499.85

(1.575)5449.62*(1.810)

0.7069**(9.177)0.8056**(10.240)0.9595**(10.054)1.1394**(5.666)1,0625**(8.268)1.1163**(14.080)1.1296**(14.589)1.2993**(13.597)1.1356**(12.813)0.0552(1.559)0.0635*

(1.780)0.0701*(1.864)denotes significance al 10 percent level,**denotes significance at 1 percent lev el.Note: t-statistics in parentheses.

Figure I. Neighborhood housing price contour: estimated 1986 price per square foot.


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4 0 6 J A N E T F U R M A N S P E Y RE R A N D WADE R . RAGAS

and may be viewed by the market as an inconvenience cost that further reduces propertyvalues. Three-dimensional representations allow the researcher to see the model with anyclusters of outlyers and to gain an intuitive understanding of the analysis.In order to examine the impact of flooding on the capitalization of insurance cost, themodel was estimated for two time periods: the entire period and the period after the first

of three significant floods. Th e F-tests for the semi-log regressions of the model after floodingoccurred relative to the entire time period (F j 1,94 = 5.60 in the subu rban neigh borho od;^1,732 = 9.67 in the urban neighborhood) are significant at the 5 percent level.* The F-tests for the linear models in the same areas (/^i,ii94 = 96.11 in the suburban neighborhood;F1732 = 3.98 in the urban neighborhood) are also significant. These data indicate thatunexpected flooding does increase the negative impact of insurance cost on house values.On the other hand, similar tests comparing the period following the third serious floodingincident with the entire period are less conclusive. The F-tests for all but the linear regres-sion in the suburban neighborhood are insignificant. The repeated incidence of floodingdoes not seem to affect the capitalization of flood insurance cost into house values. Thereare two possible explanations for this finding. First, once flooding could be anticipated,housing markets would have already capitalized the damage. Second, our survey ofhomeowners in the area found little serious flood damage to homes. In feet, the most com-

monly reported flood-relat


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HOUSING PRICES AND FLOOD RISK: AN EXAMINATION USING SPLINE REGRESSION 4 0 7

Notes1. The suburban area is bounded by Causeway Boulevard, Meadowdale Avenue, Ptwer Boulevard, and Lake

Pontchanrain. The urban area is bounded by Carrollton Avenue, South Claiborae Avenue, the, Mississippi River,and General Pershing Street.

2 . Source: FEMA map index,3. Distance fTom the lake is represented by a dummy variable which is one within four blocks from the lakeand zero elsewhere.4 . The high cost of insuring the contents of the home, if included in the cost of insurance to be capitalired,

would increase the implied discount rate found here. However, contents insurance is not mandatory and thereforenot included in this analysis.

5. A three-dimensional representation of the suburban sem i-log regression predicted values was prepared afterconvening the prices per square foot of living area for each observation to 1986 nominal dollars. The grid factorallows roughly 16 data points per squ are block. Using the graphing package SU Rf TR (4) by Golden Softwareof Golden, Colorado, with bezier spline smoothing, the graph can be magnified, rotated, shifted, and scaledon a microcomputer in MS DOS 3.0 or higher.

6. A value of 3.85 is required for significance at the 5 percent level.

ReferencesAnderson, John E. "Cubic-Spline Uiban-Density Functions." Journal of Urban Economics 12 (September 1982),155-167.Anselin, Luc. "Non-N ested Tests on the Weight Structure in Spatial Autoregressive Models: Som e Monte CarloResults." Journal of Regional Science 26 (May 1986), 267-284.Anselin, Luc, and Griffith, Daniel. "Do Spatial Effects Really Matter in Regression Analysis?" Papers of the

Regional Science Association 65 (1988), 11-34.Cassel, Eric, and Mendelsohn, Roben. "The Choice of Funaional Forms for Hedonic Price Equations: Com-

ment." Journal of Urban Economics 18 (September 1985), 135-142.Foley, James D., and \ ^ Dam, Andries. Fundamentals qf Irueractive Computer Graphics . Reading, MA: Addison-

Wesley Publishing Co., 1982.Haining, Robert. "Spatial Models and Regional Science: A Comment on Anselin's Paper and Research Direc-

tions." Journal of Regional Science 26 (November 1986), 793-798.MacDonald, D on N., Murdoch, James C , and White, Harry L. "Uncertain Hazards, Insurance, and Consumer

Choice: Evidence from Housing Markets." Land Economics b'i (November 1987), 361-371.McNeil, Donald R., Tnissel, T.James, and Turner, John C. "Spline Interpolation of Demographic Data."

Demography 14 (May 1977), 245-253.Shilling, James D., Sirmans, C.F., and Benjamin, John D. "Flood Insurance, Wealth Redistribution, and UrbanProperty Values." Journal of Urban Economics 26 (July 1989), 43-5 3 .

Skantz, TerranceR., and Strickland, Thomas H. "House Prices and a Flood Event: An Empirical Investigationof Market Efficiency." The Journal of Real Estate Research 2 (Winter 1987), 75-83.Suits, Daniel B ., Mason, Andrew, and Chan, Louis. "Spline Fun ctions Fitted by Standard Regression Methods."TJie Review of Economics and Statistics 60 (February 1978), 132-139.

Wheaton, William C. "A Bid Rent Approach to Housing Demand." Journal of Urban Economics 4 (April 1977),200-217.


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