Time on Market and Demand for Real Estate (Characteristics) Daniel Sager, Meta-Sys AG, Zurich...
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Transcript of Time on Market and Demand for Real Estate (Characteristics) Daniel Sager, Meta-Sys AG, Zurich...
Time on Market and Demand for Real Estate (Characteristics)
Daniel Sager, Meta-Sys AG, Zurich
European Real Estate Society 20th Annual ConferenceVienna, AustriaJuly 3-6, 2013
1
Demand for real estate is difficult to measure.
Vacancy rates are one-sided, there is only limited potential to measure excess demand.
Time on market can help.
What is this Study about?
I. Theoretical model
II. The natural rate of TOM
III. Empirical model
IV. Results
V. Conclusion
Literature
Table of Contents
I. Theoretical modelMoving (Wheaton (1990))
π π‘1=(1βπ1β 2 )π π‘ β1
1 +π1ππ‘ β11 +π2β 1ππ‘ β1
2(1)
where
households of type 1 in houses of type 1 (βmatchedβ)households of type 2 in houses of type 1 searching in market 2
(βsearchingβ)rate of type 2 households becoming type 1matching rate of households searching in market 1 (Poisson)
ππ‘1=π2β1ππ‘ β1
2 +(1βπ1βπ1β 2)ππ‘β 11 (2)
Matching households in market 1
Searching households in market 1
I. Theoretical modelStock flow (Poterba (1984))
(3)
where aggregate demand for houses of type 1) average demand per household for houses of type 1
rent for houses of type 1other variables affecting demandthe quality index of house type 1
price of house type 1marginal cost of production of new houses (convex!)new houses of type 1
π π‘1=π (πΌ π‘1 ) (4)
(effective) demand
production of new houses
π»π‘π ,1= (π π‘
1+ππ‘2) h1 (π π‘
1 , ππ‘ ,π1 )
I. Theoretical modelStock flow (Poterba (1984))
(5)
(6)
π»π‘1= (1βπΏ ) π»π‘+1
1 +πΌ π‘1
where stock of houses of type 1
rate of depreciation
π π‘+1β1 π π‘
1+π π‘1=π π π‘
1
where (gross) capitalization rate
Evolution of the stock of houses
Capital market equilibrium
I. Theoretical modelSome specials
Β«notionalΒ» demand
π π‘1=(1+π )π π‘ β1
ππ , 1
where measure of product market power
market clearing rent under perfect competition
where vacancy rate
π£π‘=π»π‘
1βπ»π‘π , 1
π»π‘1
π»π‘π ,1 ,πππ‘πππππ=(π π‘
1+ππ‘1 )h (π π‘
1, π π‘ ,π1 )
Myopic rent setting with product market power
Vacancies
(7)
(8)
I. Theoretical modelSome specials
Capital market equilibrium including vacancies
Adaptive expectations
π π‘+1βπ ,1 π π‘
1+π π‘1(1βπ£π‘)=π π π‘
1
π π‘+1π ,1=π π π‘
1+(1βπ)ππ‘π, 1
where rate of adaptation of expectations
(8)
(9)
I. Theoretical modelTime on market
ππ‘1=
π1ππ‘1h1(.)
π»π‘1βπ»π‘
π ,1+π2ππ‘2h2( .)
Offers (Poisson)
(10)
moving in
moving outvacancy
Time on Market
π‘πππ‘=1
ππ‘1 (11)
I. Theoretical modelTime on market: steady state comparative statics
π‘πππ‘=1ππ‘1=
π» π‘1βπ»π‘
π ,1( .,π1β2 ,π2β1)+π2ππ‘2(π1β2 ,π2β1)h
2 ( . )π1ππ‘
1(π1β2,π2β1)h1( .) (12)
πΏπ‘πππ‘
πΏπ1β2>0
πΏπ‘πππ‘
πΏπ2β1<0
πΏπ‘πππ‘
πΏππ₯β π¦
β€0π1β2=π2β 1
πΏπ‘πππ‘
πΏπ>0
I. Theoretical modelTime on market and vacancy: dynamics after demand shock
II. The natural rate of TOM
π‘ππβ=π β² π§π
οΏ½ΜοΏ½=πβ² π§βπβπ‘ππ
οΏ½ΜοΏ½=π (π‘ππβ (π§ )β π‘ππ)
depends for example on
(13)
(14)
(15)
AdScan Database: All Swiss online advertisments (real estate) since 2004Time on market <- time advertised
divide Switzerland in 25 market regions:
11 Β«largeΒ» agglomerations7 regions with smaller agglomerations7 rural areas
III. Empirical modelData
Estimate price growthHedonic regression with exogeneous variables (Β«market segmentΒ»): price level at zip, rooms, market region, new/renovated vs other, house or apartmentEstimate value of base portfolio for consecutive periods for market regions
III. Empirical modelEstimations
N = 1β103β391F( 9,1103381) = 5275.58
Dependent variable: Logarithm of time on market
robust regression
Estimate equilibrium TOMCoefficient Standard Error t
price growth -7.011 0.06312 -111.1
Number of Rooms2 -0.105 0.00435 -24.23 0.115 0.00406 28.44 0.295 0.00422 69.95 0.352 0.00556 63.36-9 0.524 0.00766 68.4
Popuation density -0.024 0.00029 -82.0Agglomeration -0.058 0.00347 -16.8House (not Apartment) -0.180 0.00529 -34.0Constant 10.392 0.06398 162.4
III. Empirical modelDemand indicator
Calculate deviation from equilibrium TOM for each real estate object
derive aggregate descriptive statics for market region, housing types ...
or
III. Empirical modelEstimate demand of characteristics for the city of Zurich in 2012
Odds Ratio Standard Error z P(z)
house / apartment 1.453 0.606856 0.89 0.37elevator 1.212 0.128142 1.81 0.07fireplace 0.726 0.123187 -1.89 0.06
balconylarge 1.247 0.132014 2.08 0.04small 1.103 0.323384 0.33 0.74
viewlake 1.432 0.175137 2.94 0.00other 0.821 0.165480 -0.98 0.33
private landlord 1.336 0.151986 2.55 0.01wheelchair 1.079 0.279157 0.29 0.77
apartment type two storey 1.220 0.329900 0.73 0.46attic 0.939 0.265409 -0.22 0.82
other variables ... ... ... ...
N = 2β818CHI2 = 434.05
dependent variable: 1 tom housing unit < tom*0 tom housing unit > tom*
Logistic regression
IV. ResultsDemand: Federal Office of Housing: Indicator of housing market scarcity
median Dtomfor 108 market regions, classified according to 10 classes representing all market situations over the last 10 years.
IV. ResultsDemand of characteristics: Probability of being scarce in the city of Zurich
IV. ResultsDemand of characteristics: Probability of being scarce and change over time
V. Conclusions
Time on market can serve as a demand indicator, when price setting does not immediately clear the market.
It can even serve as an indicator for demand of characteristics.
Even at low vacancy, time on market shows increasing (notional) demand.
Equilibrium time on market has to be carefully described (otherwise erroneous conclusions may arise).
The extension to owner-occupied housing should be straightforward.
Potential for standardized international market analysis.
Analysis of behaviour under different market mechanisms (search efficiency etc.).
Literature
Baryla E.A. & Zumpano L.V (1995): Buyer Search Duration in the Residential Real Estate Market: The Role of the Real Estate Agent; Journal of Real Estate Research, Vol 10(1), pp. 1-13
Baryla E.A., Zumpano L.V. & Elder H.W. (2000): An Investigation of Buyer Search in the Residential Real Estate Market Under Different Market Conditions; Journal of Real Estate Research, Vol. 20(1/2), pp. 75-91
Benefield J.D., Cain C.L. & Johnson K.H. (2007): On the Relationship Between Property Price, Time-on-Market, and Photo Depictions in a Multiple Listing Service; Journal of Real Estate Finance and Economics, Vol. 43, pp. 401β422
Benefield J.D., Rutherford R.C. & Allen M.T. (2012): The Effects of Estate Sales of Residential Real Estate on Price and Marketing Time; Journal of Real Estate Finance and Economics, Vol. 45, pp. 965β981Carrillo P.E. (2012): An empirical stationary equilibrium search model of the housing market; International Economic Review, Vol. 53(1), pp. 203-234Chen J. & Rutherford R.C. (2010): Quality & Time-on-the-Market in Residential Markets; Journal of Real Estate Finance and Economics, Vol. 44, pp. 414β428
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