1 / 21

Time on Market and Demand for Real Estate (Characteristics)

Time on Market and Demand for Real Estate (Characteristics). European Real Estate Society 20 th Annual Conference Vienna, Austria July 3-6, 2013. Daniel Sager, Meta-Sys AG, Zurich. What is this Study about?. Demand for real estate is difficult to measure.

alvaro
Download Presentation

Time on Market and Demand for Real Estate (Characteristics)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Time on Market and Demand for Real Estate (Characteristics) European Real Estate Society 20th Annual ConferenceVienna, Austria July 3-6, 2013 Daniel Sager, Meta-Sys AG, Zurich

  2. What is this Study about? 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.

  3. Table of Contents • Theoretical model • The natural rate of TOM • Empirical model • Results • Conclusion • Literature

  4. I. Theoretical model Moving (Wheaton (1990)) Matching households in market 1 (1) where households of type 1 in houses of type 1 (“matched”) households of type 2 in houses of type 1 searching in market 2 (“searching”) rate of type 2 households becoming type 1 matching rate of households searching in market 1 (Poisson) Searching households in market 1 (2)

  5. I. Theoretical model Stock flow (Poterba (1984)) (effective) demand (3) where aggregate demand for houses of type 1 ) average demand per household for houses of type 1 rent for houses of type 1 other variables affecting demand the quality index of house type 1 production of new houses (4) price of house type 1 marginal cost of production of new houses (convex!) new houses of type 1

  6. I. Theoretical model Stock flow (Poterba (1984)) Evolution of the stock of houses (5) where stock of houses of type 1 rate of depreciation Capital market equilibrium (6) where (gross) capitalization rate

  7. I. Theoretical model Some specials «notional» demand Myopic rent setting with product market power (7) where measure of product market power market clearing rent under perfect competition Vacancies (8) where vacancy rate

  8. I. Theoretical model Some specials Capital market equilibrium including vacancies (8) Adaptive expectations (9) where rate of adaptation of expectations

  9. I. Theoretical model Time on market Offers (Poisson) moving in (10) moving out vacancy Time on Market (11)

  10. I. Theoretical model Time on market: steady state comparative statics (12)

  11. I. Theoretical model Time on market and vacancy: dynamics after demand shock

  12. II. The natural rate of TOM (13) (14) (15) depends for example on

  13. III. Empirical model Data AdScan Database: All Swiss online advertisments (real estate) since 2004 Time on market <- time advertised divide Switzerland in 25 market regions: 11 «large» agglomerations 7 regions with smaller agglomerations 7 rural areas

  14. III. Empirical model Estimations Estimate price growth Hedonic regression with exogeneous variables («market segment»): price level at zip, rooms, market region, new/renovated vs other, house or apartment Estimate value of base portfolio for consecutive periods for market regions Estimate equilibrium TOM robust regression Dependent variable: Logarithm of time on market N = 1’103’391 F( 9,1103381) = 5275.58

  15. III. Empirical model Demand indicator Calculate deviation from equilibrium TOM for each real estate object derive aggregate descriptive statics for market region, housing types ... or

  16. III. Empirical model Estimate demand of characteristics for the city of Zurich in 2012 Logistic regression dependent variable: 1 tom housing unit < tom* 0 tom housing unit > tom* N = 2’818 CHI2 = 434.05

  17. IV. Results Demand: Federal Office of Housing: Indicator of housing market scarcity median Dtom for 108 market regions, classified according to 10 classes representing all market situations over the last 10 years.

  18. IV. Results Demand of characteristics: Probability of being scarce in the city of Zurich

  19. IV. Results Demand of characteristics: Probability of being scarce and change over time

  20. 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. The extension to owner-occupied housing should be straightforward. Equilibrium time on market has to be carefully described (otherwise erroneous conclusions may arise). Analysis of behaviour under different market mechanisms (search efficiency etc.). Potential for standardized international market analysis.

  21. Literature

More Related