Presented by Ekaterina Chernobaipage 1ERES Conference 2010 (6/26/2010) 1 Ekaterina Chernobai California State Polytechnic University, Pomona, USA College of Business Administration Department of Finance, Real Estate, and Law University of Nürtingen, Germany Department of Real Estate Management Consumption of real assets and the clientele effect Anna Chernobai Syracuse University, USA Whitman School of Management Department of Finance

Motivation Presented by Ekaterina Chernobaipage 2 Financial assets Stocks, bonds Monetary benefits to holders “Clientele effect”: Long-horizon investors buy illiquid assets; bid price down to compensate for future transaction costs; high returns (Vice versa for short-horizon investors) Long- & short-horizon investors Liquid & illiquid assets Real estate assets Residential real estate Monetary & non-monetary benefits (=utility from consumption) to holders “Clientele effect”: Long- & short-horizon house buyers Different liquidity houses Illiquid house: bidding the price down is not the only compensation for illiquidity. Can also compensate with higher utility given the right amount of search Amihud & Mendelson (1986, 1991) Also: Miller-Modigliani (1961) ERES Conference 2010 (6/26/2010)

Motivation Presented by Ekaterina Chernobaipage 3 Does Clientele Effect exist for real assets, which are characterized by heterogeneous valuations, utility from consumption, and have no investment motive ? Which type of houses is purchased by which type of buyers (by holding period)? ERES Conference 2010 (6/26/2010)

The Model ■ Theoretical model of illiquidity in residential housing markets Krainer & LeRoy (ET 2002) ■ Key features in our model : selling price time on the market proportions of houses by type proportions of households by class GENERAL EQUILIBRIUM : BUYERS & SELLERS GENERAL EQUILIBRIUM : BUYERS & SELLERS 2 TYPES OF HOUSES COMPETITION Presented by Ekaterina Chernobaipage 4 2 CLASSES OF HOUSEHOLDS UNCERTAINTY ERES Conference 2010 (6/26/2010)

The Model 2 TYPES OF HOUSES 2 TYPES OF HOUSES 2 CLASSES OF HOUSEHOLDS Presented by Ekaterina Chernobaipage 5 Short-tenure (S) e.g., Expect to move out in 1-5 years Long-tenure (L) e.g., Expect to move out in years Good (H G ) Higher potential utility Bad (H B ) Lower potential utility ? ? ? ? Search-and-match model ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 6 The Model ■ Agents differ in their expected housing tenure Short-tenure agents ( S ) Long-tenure agents ( L ) Probability (preserve match with housing services during a given period): π S Probability (preserve match with housing services during a given period): π L < ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 7 The Model ■ Houses differ in max amount of services they can provide Distribution of ε reflects heterogeneity Good houses ( H G ) Bad houses ( H B ) Prospective buyer’s drawn “fit:” ε 1 ~ Uniform [ 0, 1 ] Prospective buyer’s drawn “fit:” ε 2 ~ Uniform [ 0, θ ] 0 < θ < 1 ERES Conference 2010 (6/26/2010)

The Model Presented by Ekaterina Chernobaipage 8 ■ Key assumptions: ● Houses have only consumption value, no investment value ● Can buy or sell only 1 house per period ● Home choice problem, not a homeownership problem ● Buyers ex ante do not observe level of services of houses - Do NOT know if a house is Good or Bad - Only know that in the economy, P(H G ) = P(H B ) = 0.5 ● Sellers do not observe the type of buyers - Do NOT know if a buyer is Short-tenure or Long-tenure - Only know that in the economy, P(S) = P(L) = 0.5 ERES Conference 2010 (6/26/2010)

The Model Presented by Ekaterina Chernobaipage 9 simultaneously Buyer & Seller ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 10 The Model: Buyer’s Side Visit 2 houses randomly: Good + Bad? Good + Good? Bad + Bad? Visit 2 houses randomly: Good + Bad? Good + Good? Bad + Bad? Buy 1 house ■ In every period t of house-searching process: Don’t buy either; Keep searching in next period t+1 Don’t buy either; Keep searching in next period t+1 or Search option has value ! ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 11 The Model: Buyer’s Side  Household LIKES a house if: For each class (Short-term, Long-term) and house type (Good, Bad): ● Marginal Probability (like G ) = (1 – ε G ) Probability (Like G | visit G) = ● Marginal Probability (like B ) = (1 – ε B /θ ) Probability (Like G | visit G) = ● ε G, ε B each depends on household class: Short-term or Long-term ● Reservation fit is positively related to sales price observed fit ≥ reservation fit ε ε observed fit ≥ reservation fit ε ε ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 12 The Model: Buyer’s Side  Household LIKES a house  does not guarantee purchase For each class (Short-term, Long-term) and house type (Good, Bad): ● Availability factor – negatively related to competition ● Determined endogenously P r(BUY a house) = P r(LIKE a house) x Availability factor μ l a P r(BUY a house) = P r(LIKE a house) x Availability factor μ l a ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 13 The Model: Buyer’s Side  Household’s search option value, s : For each class (Short-term, Long-term): s and s * = search option value during t, during t+1 μ G and μ B = per-period probability of house H G and H B p G and p B = selling price of house H G and H B β = discount factor v(ε) = life-time utility given fit ε ● Life-time Utility v(ε) : v(ε) = β ε + β π v(ε) + (1 – π) (s + q) [] ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 14 The Model: Buyer’s Side  Buyer’s dilemma: For each class (Short-term, Long-term): ● Buyer’s F.O.C.: Utility( ε) – price = discounted S + value of choice Net life-time utility > 0 ● F.O.C. depends on: House type (Good, Bad) and buyer class (Short, Long) Choose optimal ε 1 and ε 2 to maximize search option value S Choose optimal ε 1 and ε 2 to maximize search option value S ERES Conference 2010 (6/26/2010)

 Seller’s value of house on the market, q: For each house type (Good, Bad): q and q * = value during t, during t+1 M = per-period selling probability p = selling price β = discount factor Presented by Ekaterina Chernobaipage 15 The Model: Seller’s Side q = M p + β (1 – M) q*  Seller sets a take-it-or-leave-it price  Trade-off: High price vs. longer time-on-the-market (liquidity)  Sells in period t with some probability ● M is the probability that at least 1 of the visitors wants to buy the house ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 16 The Model: Seller’s Side  Seller’s dilemma: ● Seller’s F.O.C depends on: House type (Good, Bad) and buyer class (Short, Long) Choose optimal price to maximize value of house on the market p q Choose optimal price to maximize value of house on the market p q ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 17 The Model: Nash Equilibrium Solve system of equations to compute equilibrium ● 22 equations, 22 unknowns ● Compute equilibrium values numerically ● Unique solution is attained ERES Conference 2010 (6/26/2010)

Presented by Ekaterina Chernobaipage 18 Research Questions  Research Questions:  Are prices and liquidity (time-on- the-market) for Good and Bad houses (H G and H B ) different? How?  Do short-term (S) buyers & long- term (L) buyers buy different house types (CLIENTELES)?  What is the composition of buyers & houses in the market? Our Hypotheses: price G > price B Bad houses sell faster (liquid) price G > price B Bad houses sell faster (liquid) Characteristics of buyers L: Likelihood to buy H G Likelihood to buy H B > Characteristic of buyers S: Likelihood to buy H G Likelihood to buy H B < Dominated by Short-term buyers, & Bad houses ERES Conference 2010 (6/26/2010)

page 19 Results Characteristics of Long-term buyers: Likelihood to buy H G Likelihood to buy H B > Likelihood to buy H G Likelihood to buy H B < Characteristics of Short-term buyers: Presented by Ekaterina Chernobai Myers and Pitkin (1995): frequently transacted homes are more likely to be “starter” homes owned by higher-mobility young households McCarthy (1976), Clark and Onaka (1983), and Ermisch, Findlay and Gibb (1996): positive relation b/w housing demand & household age, and a negative relation b/w the two & mobility ERES Conference 2010 (6/26/2010)

θ : Max level of services from partial-utility house μ : Per-period probability to buy this house type –, – –, --- : Expected tenure (S) is 2, 2.5, 3 page 20 Results θ = 0.9 θ = 0.75 (very similar houses) (different houses) μ G / μ B indifferent Long Short Long Short Long Short E[net utility] G – E[net utility] B Long Short

page 21 Results Presented by Ekaterina Chernobai price Good > price Bad “Bad” houses sell faster (more liquid) Past literature: Mixed results on the relationship b/w price & time-on-the-market Haurin (1998): “house with a value of [the atypicality index] being two standard deviations above the mean is predicted to take 20% longer to sell than would the typical house”. ERES Conference 2010 (6/26/2010)

θ : Max level of services from partial-utility house p,TOM : House price, Expected time on the market –, – –, --- : Expected tenure (S) is 2, 2.5, 3 page 22 Results θ = 0.9 θ = 0.75 (very similar houses) (different houses) p G, p B TOM G, TOM B Good Bad Good Bad Good Bad Good Bad

page 23 Results Presented by Ekaterina Chernobai The market is dominated by: - “Bad” houses - Short-term buyers Englund, Quigley and Redfearn (1999): in Sweden different types of dwellings have different price paths. Bias in repeat sales price index: track smaller, more modest homes that transact more often, rather than the aggregate housing stock. Jansen, de Vries, Coolen, Lamain and Boelhouwer (2008): in the Netherlands, 30% of the apartments (i.e., low quality) were sold at least twice during the period of study, while the proportion of detached homes (i.e., high quality) sold was at mere 7%. Case & Shiller (1987), Shiller (1991), Case, Pollakowski & Wachter (1991), Goetzmann (1992), Dreiman & Pennington-Cross (2004) ERES Conference 2010 (6/26/2010)

page 24 Results θ = 0.9 θ = 0.75 (very similar houses) (different houses) proportion L, proportion S proportion G, proportion B Long Short Good Bad Long Short Good Bad 0.5 θ : Max level of services from partial-utility house –, – –, --- : Expected tenure (S) is 2, 2.5, 3

Presented by Ekaterina Chernobaipage 25 Summary of Main Results - (Theoretical) Clientele effect: Long-term buyers prefer “good” homes Short-term buyers prefer “bad” homes Only consumption incentive Heterogeneous valuations of houses - Prices and liquidity: P G > P B and TOM G > TOM B Net expected utility compensates for higher price of illiquid (=“good”) houses As expected tenure(L)   P G , P B  and TOM G , TOM B  - Composition of houses & buyers on the market: Dominated by “bad” houses & Short-term buyers ERES Conference 2010 (6/26/2010)