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Consumption of real assets and the clientele effect

Consumption of real assets and the clientele effect. 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. Anna Chernobai

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Consumption of real assets and the clientele effect

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  1. Consumption of real assets and the clientele effect • 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 Anna Chernobai Syracuse University, USA Whitman School of Management Department of Finance

  2. Motivation Real estate assets Residential real estate Financial assets Stocks, bonds • Monetary benefits to holders • “Clientele effect”: • Monetary & non-monetary benefits (=utility from consumption) to holders • “Clientele effect”: Liquid & illiquid assets Long- & short-horizon investors Different liquidity houses Long- & short-horizon house buyers Long-horizon investors buy illiquid assets; bid price down to compensate for future transaction costs; high returns (Vice versa for short-horizon investors) 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) Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

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

  4. 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 2 CLASSES OF HOUSEHOLDS 2 TYPES OF HOUSES GENERAL EQUILIBRIUM: BUYERS & SELLERS COMPETITION UNCERTAINTY Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010) page 4

  5. The Model Search-and-match model 2 CLASSES OF HOUSEHOLDS 2 TYPES OF HOUSES ? Short-tenure (S) e.g., Expect to move out in 1-5 years Long-tenure (L) e.g., Expect to move out in 20-25 years Good (HG) Higher potential utility Bad (HB) Lower potential utility ? ? ? Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  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): πS Probability (preserve match with housing services during a given period): πL < Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  7. The Model • Houses differ in max amount of services they can provide • Distribution of ε reflects heterogeneity Good houses (HG) Bad houses ( HB) Prospective buyer’s drawn “fit:” ε1~ Uniform [ 0, 1 ] Prospective buyer’s drawn “fit:” ε2~ Uniform [ 0, θ] 0 < θ < 1 Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  8. The Model • 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 antedo notobservelevel of services of houses • - Do NOT know if a house is Good or Bad • - Only know thatin the economy, P(HG) = P(HB) = 0.5 • ● Sellersdo not observe the type of buyers • - Do NOT know if a buyer is Short-tenure or Long-tenure • - Only know thatin the economy, P(S) = P(L) = 0.5 Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

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

  10. The Model: Buyer’s Side • In every period t of house-searching process: Buy 1 house Visit2 houses randomly: Good + Bad? Good + Good? Bad + Bad? or Don’t buy either; Keep searching in next period t+1 Search option has value ! Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  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 – εG) • Probability (Like G | visit G) = • ● Marginal Probability (like B ) = (1 – εB/θ) • Probability (Like G | visit G) = • ● εG , εBeachdepends on household class: Short-term or Long-term • ● Reservation fit is positively related to sales price observed fit ≥ reservation fit ε ε Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  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 Pr(BUY a house) =Pr(LIKE a house) xAvailability factor μ l a Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  13. The Model: Buyer’s Side • Household’s search option value, s : • For each class (Short-term , Long-term): • sand s* = search option value during t, during t+1 • μG and μB= per-period probability of house HG and HB • pG and pB = selling price of house HG and HB • β = discount factor • v(ε)= life-time utility given fit ε • ● Life-time Utilityv(ε) : [ ] v(ε) = β ε + βπ v(ε) + (1 – π) (s + q) Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  14. The Model: Buyer’s Side • Buyer’s dilemma: • For each class (Short-term , Long-term): • ● Buyer’sF.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 ε1 and ε2 to maximize search option value S Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

  15. The Model: Seller’s Side • 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 • 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 q = M p + β(1 – M) q* ●M is the probability that at least 1 of the visitors wants to buy the house Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

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

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

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

  19. Results Characteristics of Long-term buyers: Likelihood to buy HG Likelihood to buy HB > Characteristics of Short-term buyers: Likelihood to buy HB Likelihood to buy HG < 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 Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010) page 19

  20. Results θ = 0.9 θ = 0.75 (very similar houses) (different houses) μG / μB Long Long indifferent indifferent Short Short E[net utility]G – E[net utility]B Long Long Short Short 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

  21. Results priceGood > priceBad “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”. Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010) page 21

  22. Results θ = 0.9 θ = 0.75 (very similar houses) (different houses) pG , pB Good Good Bad Bad TOMG , TOMB Good Good Bad Bad 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

  23. Results 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) Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010) page 23

  24. Results θ = 0.9 θ = 0.75 (very similar houses) (different houses) proportionL, proportionS Short Short 0.5 0.5 Long Long proportionG, proportionB Bad Bad 0.5 0.5 Good Good θ : Max level of services from partial-utility house – , – – , --- : Expected tenure (S) is 2, 2.5, 3

  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: PG> PB and TOMG > TOMB Net expected utility compensates for higher price of illiquid (=“good”) houses As expected tenure(L) PG, PB  and TOMG, TOMB - Composition of houses & buyers on the market: Dominated by “bad” houses & Short-term buyers Presented by Ekaterina Chernobai ERES Conference 2010 (6/26/2010)

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