地产中介:拐点未到

房地产的冬天真的来了么?地产中介们并不那么认为。去年下半年开始,"房地产进入拐点时期"的声音就不绝于耳,去年11月,深圳"中天置业"总裁蒋飞携款潜逃,连续几家大规模的房地产中介倒闭,今年1月,北京信一天     

How Do Land Auction Formats Influence the Market Structure and Aggregate Surplus of Real Estate Development?

This article proposes a duopoly dynamic game theoretic model to investigate the market structure and aggregate surplus of real estate development when land is sold in a sealed‐bid first price auction vis‐à‐vis an open English auction. It relaxes the assumption of symmetric bidders. The land values have common value and private value components. We find that the sealed‐bid first price auction introduces competition in the real estate development market. The open English auction leads a monopoly market. State agencies are recommended to increase the aggregate surplus of real estate development by publishing past bidding information under the sealed‐bid first price auction and reducing information asymmetry between bidders.     

The Gender Gap in Real Estate Sales: Negotiation Skill or Agent Selection?

This study examines differences in net selling price for residential real estate across male and female agents. A sample of 2,020 home sales transactions from Fulton County, Georgia, are analyzed in a two‐stage least squares, geospatial autoregressive corrected, semi‐log hedonic model to test for gender and gender selection effects. Although agent gender seems to play a role in naïve models, its role becomes inconclusive as variables controlling for possible price and time on market expectations of the buyers and sellers are introduced to the models. Clear differences in real estate sales prices, time on market and agent incomes across genders are unlikely due to differences in negotiation performance between genders or the mix of genders in a two‐agent negotiation. The evidence suggests an interesting alternative to agent performance: that buyers and sellers with different reservation price and time on market expectations, such as those selling foreclosure homes, tend to select agents along gender lines.     

Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model

In this study we consider the problem of sellers, buyers and real estate appraisers in determining the price for a house, taking into account the characteristics of the house and its location as well as the goals of these three different parties. The appraiser's job is to determine the fair market value of the house, while the buyer and seller want to find, respectively, the lowest and highest feasible price for it. We combine recent developments in geography and econometrics to develop an approach that determines local estimates of property values from the perspectives of the buyer, seller and appraiser, taking into account the characteristics of the house as well as its location. We illustrate our approach analyzing closing prices in one residential real estate market.     

Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach

The absolute location of each real estate parcel in an urban housing market has a unique location-value signature. Accessibility indices, distant gradients and locational dummies cannot fully account for the influence of absolute location on the market price of housing because there are an indeterminable number of externalities (local and nonlocal) influencing a given property at a given location. Furthermore, the degree to which externalities affect real estate values is not only unique at each location but highly variable over space. Hence, absolute location must be viewed as interactive with other determinants of housing value. We present an interactive variables approach and test its ability to explain price variations in an urban residential housing market. The statistical evidence suggests that the value of location, as embodied in the selling price of housing units, may not be separable from other determinants of value. It is recommended that housing valuation models, therefore, be specified to allow site, structural and other independent attributes to interact with absolute location—{ x , y } coordinates—when accounting for intraurban variation in the market price of residential housing. This approach is especially useful when estimating the value of housing for geographic areas where very little is known a priori about the neighborhoods or submarkets.     

Rational Expectations, Market Fundamentals and Housing Price Volatility

This paper derives a forward-looking rational expectations house price model and empirically tests its ability to explain short-run fluctuations in real house prices. A novel approach to proxying the imputed rents of owner-occupied housing, as a function of observable housing market fundamentals, is combined with a housing market arbitrage relation to derive a present value model for real house prices. Tests of the rational expectations, nonlinear cross-equation restrictions reject the joint null hypothesis of rational expectations and the asset-based housing price model for quarterly, single-detached house prices in the city of Vancouver, British Columbia from 1979–1991. The model fails to fully capture observed house price dynamics in two real estate booms but tracks real house prices well in less volatile times, suggesting that prices may temporarily deviate from fundamental values in real estate price cycles.     

A Model of Time‐on‐Market and Real Estate Price Under Sequential Search with Recall

This article develops a model and provides a closed‐form formula to uncover the theoretical relationship between real estate price and time on market (TOM). Our model shows a nonlinear positive price‐TOM relationship, and it identifies three economic factors that affect the impact of TOM on sale price. We demonstrate that conventional metrics for real estate return and risk, which are borrowed in a naïve fashion from finance theory, do not account for marketing period risk and tend to overestimate real estate returns and underestimate real estate risks. Our model provides a simple way to correct such bias. This theory helps to explain the apparent “risk‐premium puzzle” in real estate.     

Economic Depreciation of Residential Real Estate: Microlevel Space and Time Analysis

Three elements in the study of real estate depreciation that warrant further consideration are uncovered: the spatial variation of depreciation on a micro scale, the variability of depreciation within a single market across time and the recognition of land value as an influence in modeling real property prices. Taken together, these three dimensions provide an opportunity to further expand the understanding of residential economic depreciation while enhancing the predictive power of real estate market models. The analytical results, utilizing a land-value-adjusted hedonic model, indicate that both the intramarket location and the year in which the property sold have significant impacts on the observed rate of economic depreciation. Such information is vitally important to policymakers and others interested in accurate modeling of real estate markets.     

Contracts as Options: Some Evidence from Condominium Developments

This paper values the real estate option to purchase contract in a contingent claims framework. The model is an application of the Black and Scholes option pricing model. Observed market data on the sale of condominiums are used to test if the option is competitively priced under various assumptions regarding the expected instantaneous variance of the condominium price. Simulation results suggest that standard industry practices of charging a fixed amount for the option to purchase roughly conform to pricing behavior dictated by the option pricing model.     

Market Efficiency and Return Statistics: Evidence from Real Estate and Stock Markets Using a Present-Value Approach

This paper develops a methodology to identify asset price response to news in the framework of the Campbell–Shiller log-linear present-value equation. We further show that a slow price adjustment in real estate markets not only induces a high serial autocorrelation in excess returns, but also dampens the return volatility and the correlation with excess returns in other asset markets. Using Hong Kong real estate and stock market data, we find that the quarterly real estate price assimilates only about half the effect of market news, whereas the quarterly stock price incorporates the news fully. Our analysis identifies a cumulative price adjustment that recovers lost information in real estate returns due to market inefficiency and thereby restores the real estate return volatility and the correlation between real estate and stock markets.     

Illiquidity and Pricing Biases in the Real Estate Market

This article addresses the micro-analytic foundations of illiquidity and price dynamics in the real estate market by integrating modern portfolio theory with models describing the real estate transaction process. Based on the notion that real estate is a heterogeneous good that is traded in decentralized markets and that transactions in these markets are often characterized by costly searches, we argue that the most important aspects defining real estate illiquidity in both residential and commercial markets are the time required for sale and the uncertainty of the marketing period. These aspects provide two sources of bias in the commonly adopted methods of real estate valuation, which are based solely on the prices of sold properties and implicitly assume immediate execution. We demonstrate that estimated returns must be biased upward and risks downward. These biases can be significant, especially when the marketing period is highly uncertain relative to the holding period. We also find that real estate risk is closely related to investors' time horizons, specifically that real estate risk decreases when the holding period increases. These results are consistent with the conventional wisdom that real estate is more favorable to long-term investors than to short-term investors. They also provide a theoretical foundation for the recent econometric literature, which finds evidence of smoothing of real estate returns. Our findings help explain the apparent risk-premium puzzle in real estate—that is, that ex post returns appear too high, given their apparent low volatility—and can lead to the formal derivation of adjustments that can define real estate's proper role in the mixed-asset portfolio.     

Determinants of the Rate of Return for Nonresidential Real Estate: Inflation Expectations and Market Adjustment Lags

This paper analyzes the economic forces that determine the real rate of return for nonresidential real estate. Our analysis shows that the intermarket variation in the real rate of return is statistically significant, and the rate of return differs by land use and market area, as well as over time in response to changes in macro-economic conditions. We use inflation variables as surrogates for changes in macroeconomic conditions over time. In contrast to earlier studies, we find that nonresidential real estate may not outperform expected inflation. We believe that the impact of expected inflation (and other macroeconomic variables) on real estate rates of return depends upon the interaction of the macro-environment and specific local real estate market conditions. Finally, our empirical evidence suggests that the effects of a given shock dissipate rather quickly in most markets when we take into account locational and property use differences.     

How to Set Minimum Acceptable Bids, with an Application to Real Estate Auctions

In a general auction model with affiliated signals, common components to valuations and endogenous entry, we compute the equilibrium bidding strategies and outcomes, and derive a lower bound on the optimal reserve price. This lower bound can be computed using data on past auctions combined with information about the subsequent sales prices of unsold goods. We illustrate how to compute the lower bound using data from real estate auctions.     

Real Estate Returns and Risk with Heterogeneous Investors

This article develops a theoretical framework and formulates a unified risk metric that integrates both real estate price risk and uncertainty of time on market (TOM). We demonstrate that real estate sellers with different degrees of financial distress face not only different marketing period risks, but also receive different return distributions upon successful sales. The major findings of this article can be summarized as follows. First, we show that real estate return and risk, which account for both price and TOM risk, are investor specific, varying over investors with different financial circumstances and holding periods. Second, the traditional valuation of real estate return and risk, which is based solely on the return distribution of a successful sale without considering the uncertainty of TOM and the investor's financial circumstances, underestimates real estate risk and exaggerates real estate return. Third, our empirical applications in both residential and commercial real estate markets show that the Sharpe ratio estimated by the traditional approach is seriously overstated—to the largest extent for investors with high financial distress. In addition, we find that, given the typical 5‐ to 7‐year holding period for real estate, the Sharpe ratios estimated by integrating both price and TOM risk are much in line with the performance of financial assets. These findings can help to explain the apparent “risk‐premium puzzle” in real estate.     

Transactions-Driven Commercial Real Estate Returns: The Panacea to Asset Allocation Models?

A transactions-driven commercial real estate return series is generated in this study to determine whether the reliance on appraised values in the estimation of real estate returns is the source of the reported underpricing of real estate relative to stocks, bonds, and bills when analyzed in a traditional mean-variance setting. The reported underpricing of commercial real estate would be rational if transactions-driven returns exhibit more variance than appraisal-driven returns. While we find that transactions-driven real estate returns have greater variance than appraisal-driven returns for individual properties, most of the individual property risk is idiosyncratic and diversified away at the portfolio level. Real estate continues to be a dominate asset class in mean-variance allocation models even when represented with transactions-driven indices.¹     

Portfolio Considerations in the Valuation of Real Estate

When a real asset rises in price faster than inflation (as real estate did in the late 1970s) and rises significantly in price over an extended period (as real estate has done for the last decade and one-half), it concerns valuation and investment professionals who fear about it being over-valued. One of the reasons for such price performance may be an increase in demand due to the portfolio characteristics of the asset during the period of time in question. For real estate this means the proportion included in optimal portfolios should be significant and increasing as individual tax rates increase in an environment of increasing average tax rates.
This study uses six tax brackets (0%, 10%, 20%, 30%, 40%, 50%) and portfolios consisting of three traditional assets (NYSE common stocks, corporate bonds and small stocks) plus three types of real estate (residential, business and farmland) to demonstrate that this is what has transpired in the real estate markets. Optimal portfolio weights are derived for each asset for after-tax portfolios. Real estate in general and residential real estate especially increased as a proportion of the optimal after-tax portfolio as individual tax rates increased. Other studies are used to demonstrate an environment of increasing average tax rates.     

House Prices and Regional Real Estate Cycles: Market Adjustments in Houston

Real estate cycles often generate sharp swings in real housing prices, price changes that cannot be adequately described by a single statistic such as median home values. Instead, the entire structure of prices across all quality levels must be examined. This paper analyzes the price impact of the Houston, Texas, real estate bust. It shows that the average price of housing fell, and that the structure of the housing price function itself changed. Changes in the marginal price of housing were probably more significant to the market equilibrating process than the decline in average price alone.     

The Substitutability of Real Estate Assets

This paper investigates the degree of substitutability between securitized real estate assets and real estate assets whose prices are appraisal-based. Given the insensitivity of unsecuritized asset's returns to the returns on stock market indices, equilibrium asset pricing models cannot be used to compare these two avenues of investment. Two assets are deemed substitutable if the information sets underlying unbiased, minimum error variance estimates of their pricing parameters are identical. The empirical evidence shows that the prices of the transactions-based assets—real estate investment trusts and the stock price index of the home building industry—follow a random walk while the prices of the appraisal-based assets—FRC/NCREIF indices—do not. The variance decompositions of the vector autoregressions also show that the level of economic activity helps predict the price indices of appraisal-based assets while the stock market index and the term structure of interest rates are better predictors of the prices of transactions-based assets     

Are Brokers' Commission Rates on Home Sales Too High? A Conceptual Analysis

Many people in North America believe that prevailing commission rates for residential real estate brokers are too high, even though such beliefs are not based on a formal model. This paper presents a general equilibrium model of the housing market in which real estate brokers serve as matching intermediaries. We use this model to construct an illustrative example which is calibrated using data consistent with a typical housing market. The example suggests that the commission rate which maximizes aggregate efficiency is considerably below the prevailing rate. Moreover, this finding appears to be robust to changes in the matching process.     

Returns and Information Transmission Dynamics in Public and Private Real Estate Markets

This paper examines U.S. public and private commercial real estate returns at the aggregate level and by the four major property types over the 1994–2012 time period. Returns are carefully adjusted for differences between public and private markets in financial leverage, property type focus and management fees. Unconditionally, we find that passive portfolios of unlevered core real estate investment trusts (REITs) outperformed their private market benchmark by 49 basis points (annualized) over the 1994–2012 sample period. Our baseline vector autoregression results suggest that REIT returns do not embed additional commercial real‐estate‐specific information useful in predicting private market returns. These results strongly suggest that equity REIT returns react to fundamental (latent) asset pricing information more quickly than private market returns given their greater liquidity and price revelation. REITs therefore serve as a fundamental information transmission channel to private market returns when asset pricing variables are omitted.