1. Presented by Ekaterina Chernobaipage 1ERES 2011, Eindhoven 1 The Determinants of Buyer Search Duration in “Hot” and “Cold” Residential Real Estate Markets Ekaterina Chernobai California State Polytechnic University, Pomona, U.S.A. College of Business Administration Department of Finance, Real Estate, and Law University of Nürtingen, Germany (Visiting Professor) Department of Real Estate Management Tarique Hossain California State Polytechnic University, Pomona, U.S.A. College of Business Administration Department of International Business and Marketing

2. Presented by Ekaterina Chernobaipage 2 Idea Housing liquidity Time on the marketTime to buy Seller sideBuyer side We analyze: Determinants of search duration Differences in the effects in “hot” and “cold” housing markets Differences between private investors and non-investors Also: Effects of “subprime crisis” ERES 2011, Eindhoven

3. Presented by Ekaterina Chernobaipage 3 Idea 2004-2005 survey of recent house buyers We run:2007-2008 survey of recent house buyers Same geographic area in Southern California – most pronounced “bubble” Same average house price in both time periods January 2000 = 100 ERES 2011, Eindhoven

4. Presented by Ekaterina Chernobaipage 4 Idea Geographical coverage of the surveys San Luis Obispo county Santa Barbara county Ventura county San Francisco Los Angeles San Diego ERES 2011, Eindhoven California

5. Presented by Ekaterina Chernobaipage 5 Idea Some past studies of search duration: Case and Shiller (1988) “The Behavior of Home Buyers in Boom and Post-Boom Markets” - House purchases during 1-year period - Different geographic locations - Some with rising some with declining prices Baryla, Zumpano, Elder (2000) “An Investigation of Buyer Search in the Residential Real Estate Market under Different Market Conditions” - Houses purchased in different interest rate periods - Different geographic locations (entire country) ERES 2011, Eindhoven

6. Presented by Ekaterina Chernobaipage 6 Idea Krainer and LeRoy (2002) “Equilibrium Valuation of Illiquid Assets” - Theoretical general equilibrium model of housing transactions - Buyers’ valuations of houses are heterogeneous - Weigh the search cost against the expected life-time utility less price - Equilibrium: Longer expected stay in a house increases search duration E. Chernobai (working paper) “When Does Mobility Reduce Liquidity” - Theoretical model that generalizes Krainer & LeRoy’s - Short-term and long-term buyers - Equilibrium: Long-term buyers search longer than short-term buyers ERES 2011, Eindhoven

7. Presented by Ekaterina Chernobaipage 7 Research Questions Questions of interest: 1.) Any effect of expected housing tenure on realized search duration? 2.) Are consumption-buyers different from investor-buyers? 3.) Any differences in 1.) and 2.) between “hot” and “cold” markets? Our hypotheses: Positive relationship if buy for consumption Investors: -Time pressure to buy: Yes? No? -What does a typical consumption- buyer want? Short-term investors vs. Long-term investors - Time to buy is shorter in “hot” market - Investors: Rel. proportion of short- & long-term investors varies over time ERES 2011, Eindhoven

8. Results Presented by Ekaterina Chernobaipage 8 Survey 2004-2005 recent house-buyers Mailed 6,000 questionnaires Response rate: 11.3% (661) Survey 2007-2008 recent house-buyers Mailed 6,200 questionnaires Response rate: 11.6% (719) “Hot” market“Cold” market ERES 2011, Eindhoven

9. Results Presented by Ekaterina Chernobaipage 9 VariablePooled“Hot”“Cold”Difference in means (p-value) Time To Buy 6.805.358.060.000*** First 0.240.250.230.538 Offers 1.781.831.740.279 New 0.090.070.100.076* Miles 201.40178.49221.330.251 Price 612,625602,267621,9000.284 Moving Up 0.32 0.987 Coast 0.380.400.360.336 Fixed 0.530.410.630.000*** HighLTV 0.340.330.350.504 1-5 years 0.340.410.270.000*** 6-10 years 0.220.200.240.158 10+ years 0.370.310.420.000*** 1-5 years *Investor 0.040.070.020.001*** 6-10 years *Investor 0.01 0.020.281 10+ years*Investor 0.01 0.674 Investor 0.090.110.070.074* Winter 0.160.060.250.000*** Spring 0.220.230.220.523 Summer 0.290.320.260.060* Fall 0.320.380.270.001*** (months) Correlation matrix: no issues DESCRIPTIVE STATISTICS ERES 2011, Eindhoven

10. Results Presented by Ekaterina Chernobaipage 10 VariablePooledHot marketCold market Intercept0.899 (<0.0001)0.305 (0.1277)0.291 (0.1440)-0.434 (0.1259)0.887 (0.0008) First0.056 (0.5945)0.051 (0.6236)0.057 (0.5770)0.373 (0.0089)-0.165 (0.2216) Offers0.218 (<0.0001)0.212 (<0.0001)0.221 (<0.0001)0.195 (<0.0001)0.280 (<0.0001) New0.476 (0.0009)0.437 (0.0017)0.445 (0.0010)0.987 (<0.0001)0.124 (0.4447) Miles 0.000 (0.9190)0.000 (0.7421)0.000 (0.6030)0.000 (0.0523)0.000 (0.6554) Price 0.000 (0.0054)0.000 (0.0116)0.000 (0.0120)0.000 (0.0029)0.000 (0.4476) MovingUp -0.113 (0.2220)-0.150 (0.0991)-0.123 (0.1764)-0.284 (0.0306)0.010 (0.9276) Coast 0.236 (0.0040)0.224 (0.0055)0.227 (0.0046)0.385 (0.0007)0.227 (0.0323) Fixed 0.027 (0.7399)-0.007 (0.9335)0.012 (0.8768)0.218 (0.0508)-0.200 (0.0595) HighLTV -0.186 (0.0418)-0.143 (0.1163)-0.143 (0.1115)-0.186 (0.1783)-0.062 (0.5831) 1-5 years 0.621 (<0.0001)0.528 (0.0009)0.718 (0.0006)0.405 (0.0614) 6-10 years 0.557 (0.0007)0.560 (0.0006)0.659 (0.0038)0.487 (0.0247) 10+ years 0.866 (<0.0001)0.877 (<0.0001)0.803 (0.0002)0.787 (0.0001) 1-5 years *Investor 0.540 (0.0060)0.336 (0.1299)1.028 (0.0034) 6-10 years *Investor -0.160 (0.6475)0.617 (0.3038)-0.465 (0.2406) 10+ years*Investor -0.545 (0.1145)-0.197 (0.7039)-0.712 (0.0872) Winter 0.351 (0.0031)0.336 (0041)0.315 (0.0065)0.782 (0.0007)-0.030 (0.8263) Spring 0.180 (0.0965)0.172 (0.1027)0.148 (0.1589)0.274 (0.0519)0.099 (0.4874) Summer 0.259 (0.0102)0.254 (0.0097)0.247 (0.0110)0.340 (0.0078)0.105 (0.4400) N obs.806 375431 Dependent variable: logTimeToBuy Weibull distribution REGRESSION RESULTS consumption incentive ERES 2011, Eindhoven

11. Results Presented by Ekaterina Chernobaipage 11 We also looked at Pooled regression model with: “Hot” dummy interaction terms for all variables  significant for many  Splitting into “hot” and “cold” sub-samples is justified “Investor” dummy interaction terms for all variables  significant for Tenure variables, Coast, and New To identify consumption- and investment-driven submarkets, split “Hot” and “Cold” samples into - Coastal zip code areas (43% of zip codes, 38% of obs.) - Inland zip code areas ERES 2011, Eindhoven

12. Results Presented by Ekaterina Chernobaipage 12 Variable “Hot” (2004-2005) “Cold” (2007-2008) CoastalInlandCoastalInland Intercept -0.378 (0.3663)-0.150 (0.6918)1.099 (0.0107)0.991 (0.0019) First 0.508 (0.0663)0.387 (0.0249)-0.612 (0.0118)-0.041 (0.8009) Offers 0.176 (0.0097)0.255 (<0.0001)0.170 (0.0623)0.323 (<0.0001) New 0.297 (0.4324)1.495 (<0.0001)0.857 (0.0183)-0.009 (0.9608) Miles 0.000 (0.0942)0.000 (0.2333)0.000 (0.2430)0.000 (0.2254) Price 0.000 (0.0984)0.000 (0.1175)0.000 (0.2863)0.0000 (0.4855) MovingUp -0.097 (0.6428)-0.348 (0.0331)-0.048 (0.7961)0.188 (0.1797) Fixed 0.281 (0.1264)0.230 (0.0968)-0.074 (0.6780)-0.297 (0.0188) HighLTV -0.644 (0.0116)0.010 (0.9521)-0.189 (0.2999)-0.088 (0.5318) 1-5 years 0.986 (0.0028)0.507 (0.0503)0.192 (0.5651)0.608 (0.0270) 6-10 years 1.059 (0.0027)0.353 (0.2232)0.229 (0.4749)0.696 (0.0109) 10+ years 1.290 (<0.0001)0.456 (0.0915)0.965 (0.0014)0.742 (0.0048) 1-5 years *Investor 0.419 (0.2046)0.533 (0.0687)-0.930 (0.1218)1.399 (0.0004) 6-10 years *Investor 0.189 (0.8648)0.630 (0.3684)-0.962 (0.1176)-0.235 (0.6253) 10+ years *Investor 0.158 (0.8296)-1.258 (0.0740)-1.751 (0.0029)0.039 (0.9429) Winter 0.967 (0.0134)0.554 (0.0689)0.220 (0.3705)-0.239 (0.1256) Spring 0.540 (0.0274)0.106 (0.5366)0.217 (0.3676)0.093 (0.5740) Summer 0.499 (0.0125)0.104 (0.5301)0.110 (0.6388)0.119 (0.4519) N obs. 149226157274 Dependent variable: logTimeToBuy Weibull distribution REGRESSION RESULTS consumption incentive investment incentive - short term - investment incentive - long term - ??? next housing cycle ERES 2011, Eindhoven

13. Results Presented by Ekaterina Chernobaipage 13ERES 2011, Eindhoven One possible explanation of differences between Coast & Inland results: Hot Cold CoastInlandCoastInland VariableMeanSDMeanSDMeanSDMeanSD Search duration (months) 5.527.644.397.258.6712.6957.609.43 Purchase price ($) 618,639250,283578,151218,562627,267278,226615,884287,611 FRM financing (% of borrowers) 31.3046.5546.3449.9965.4447.7372.0644.96 Conforming loans (% of borrowers) 12.3532.9015.1535.8636.7348.2133.9847.36 Purchase price ($) 469,048201,542501,429148,267531,861182,174519,207181,868 FRM financing (% of borrowers) 50.0051.3072.7345.2375.9343.1681.8238.79 Non-conforming loans (% of borrowers) 87.6532.9084.8535.8663.2748.2166.0247.36 Purchase price ($) 644,631245,991622,959236,499652,444296,068655,288323,907 FRM financing (% of borrowers) 28.8045.4739.5349.0460.2249.2169.5946.14 (means) << < < < >

14. Summarize Presented by Ekaterina Chernobaipage 14 Consumption-driven purchases: - “hot” market in coastal areas - “cold” market in inland areas Long-horizon investment activity: - in just the opposite sub-markets Short-horizon investment activity: - in inland areas  Investment activity leads consumption activity ERES 2011, Eindhoven The boom & burst of the “sub-prime” bubble affected search duration in Hot & Cold periods