Transaction Frequency and Commercial Property

Metrics using repeat sale data assume that frequently and infrequently sold properties are similar in capital expenditures, maintenance and other characteristics. Value-added investors concentrate on repositioning properties which requires capital investment and managerial skills. Returns using repeat sales likely overstate appreciation by misattributing this investment. Present results show that frequently and infrequently traded properties represent different property populations. The first sale of a repeat transaction sells at a significant discount compared to single sale properties while the second sale transacts at a premium. The results suggest that repeat sale indices may overstate price appreciation and represent returns for a different, relatively small cohort of properties when compared to the large number of properties that transact only once during a specific time period.     

Estimating price trends for residential property: A comparison of repeat sales and assessed value methods

The repeat sales methodology for estimating residential price indices is based on actual appreciation of individual properties. On the other hand, the repeat sales method wastes data, typically discarding a large percentage of all sales. This article explores two issues related to the subsample of repeat sales. First, are paired sales representative of the entire population of properties that sold? Second, is there evidence that sample selectivity biases the price trend estimates? Evidence from five metropolitan areas supports a negative answer to the first question and the second question. It appears that a “lemon” or “starter home” effect causes repeat residential sales to be a biased subsample of all transactions. Cumulative price trends for the repeat subsamples can differ from the full samples over periods ranging from two to ten quarters. While short-term price trends can differ widely, there are no systematic differences among the samples over long periods of time (e.g., three years or more).     

The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

This paper concerns the estimation of granular property price indices in commercial real estate and residential markets. We specify and apply a repeat sales model with multiple stochastic log price trends having a hierarchical additive structure: One common log price trend and cluster specific log price trends in deviation from the common trend. Moreover, we assume that the error terms potentially have a heavy tailed (t) distribution to effectively deal with outliers. We apply the hierarchical repeat sales model on commercial properties in the Philadelphia/Baltimore region and on residential properties in a small part of Amsterdam. The results show that the hierarchical repeat sales model provides reliable indices on a very detailed level based on a small number of observations. The estimated degrees of freedom for the t-distribution is small, largely rejecting the commonly made assumption of normality of the error term.     

Intrametropolitan Decentralization: Is Government Structure Capitalized in Residential Property Values?

This paper examines the influence that the intrametropolitan growth in special districts has on residential property values. Our empirical approach tests whether the benefits of decentralizing local public good providers increases, decreases or leaves residential property appreciation rates unchanged. Past research in this area has been limited by the lack of variation in government structure within a region and by the self-selection of areas that decentralize governments. This research overcomes these limitations by 1) comparing appreciation rates for single-family homes that were located in areas that added local governments to appreciation rates for properties that were not; and 2) employing an estimation technique that border matches repeat sales to control for the self-selection of government structure. Overall, empirical results indicate that institutional decentralization has no influence on single-family property appreciation rates. It makes no difference whether the new government is the 3rd, 4th, 5th or 6th new jurisdiction–the new government does not influence appreciation rates. Residential property values for homes located in jurisdictions that added security special districts experienced rates of appreciation that were lower than otherwise comparable properties. Recreation, fire, water, sewer and other special districts had no measurable influence on appreciation rates. Empirical results also indicate that more overlap among local governments reduces appreciation rates. New governments created in areas whose residents have greater income heterogeneity increase appreciation rates. The distance separating the new government from existing governments, the land area of the new government and the creation of multiple new governments have no influence on appreciation rates. Finally, these results depend on the border matching repeat sales estimation technique employed here.     

Manufacturing versus Distribution: Implicit Pricing of Real Property Characteristics by Submarket

This article develops and tests the idea that the industrial real estate market is an aggregate market consisting of at least two submarkets—manufacturing and distribution. While there is no observable difference in implicit pricing of most industrial property characteristics across these two submarkets, some property-characteristic implicit prices do differ. Therefore, manufacturing and distribution submarket property-pricing functions are best estimated in aggregate, while making allowances for variability of coefficients on some property characteristics. For this sample of 331 industrial property sales from the southeastern region of the United States, the two submarkets vary with regard to implicit pricing of building volume, below-average building condition, site area, and dock-high doors.     

On Indexing Commercial Real Estate Properties and Portfolios

Commercial real estate indices play an important role in performance evaluation and overall investment strategy. However, the issue of how representative they are of the returns on portfolios of commercial properties is an open issue. Our study addresses this topic by analyzing a sample of 12,427 repeat sales transactions between Q4 2000 and Q2 2011. We find that the aggregate real estate indices (Moody’s REAL CPPI) do a good job of tracking real returns when portfolios of more than 20 properties are considered. At this level, tracking is somewhat less effective than our benchmark of the S&P500 and its component stocks. Compared to the average root mean squared deviation (RMSD) from one asset, randomly selected portfolios with 20 assets reduce the RMSD by 75 % for the S&P500 compared to 66 % for the aggregate index. These results suggest that the aggregate indices can be effective in hedging and evaluating the performance of direct real estate investment. We further find that tracking at the property type level provides little benefit over using an aggregate index. However, indexing using a property type and location matched index provides lower tracking error for any level of diversification.     

The Effects of Attributes on the Repeat Sales Pattern of Residential Property in Hong Kong

There has been copious research work on the development of house price models and the construction of house price indices. However, results in some studies revealed that the accuracy of such indices could be subject to selection bias when using only information from a sample of sold properties to estimate value movements for the entire housing stock. In particular, estimated house price appreciation is usually systematically higher among properties that change hands more frequently. It therefore suggests that the determination of important factors affecting the transaction frequency or intensity of a housing unit should be a more fundamental research question. This paper examines the possible factors that determine the popularity of residential unit by means of a repeated sales pattern. The Poisson regression model and event history analysis techniques are employed to assess the effect of attributes on transaction frequency and intensity. The event history analyses technique can take account of transaction-specific as well as time-dependent covariates, and therefore is recommended for analyzing repeated sales data in a real estate market. All transaction records during the period 1993–2000 from the Land Registry of one of the most popular residential estates in Hong Kong were used to illustrate the method. Unlike a response to favorable transaction price, good quality units do not necessarily inherently display a high transaction frequency. Rather, units of average quality are more likely to be transactionally active.     

Alternative Methods of Increasing the Precision of Weighted Repeat Sales House Prices Indices

Weighted repeat sales house price indices have become one of the primary indicators used to identify housing market conditions and to estimate the amount of equity homeowners have gained through house price appreciation. The primary reason for the acceptance of this methodology is that it derives a location specific (typically, census division, state or metropolitan area) average change in house prices from repeated observations of individual house prices. It is this repeat attribute that allows repeat sales price indices to claim that it is a preferable index which does a better job of holding quality constant. The amount of time between the two observed prices for a single property is determined by when the home transacts. Some homes transact twice in a period of months and others do not transact for decades. It is likely that individual house price appreciation rates vary from the mean appreciation rate, as estimated by the index, in a systematic fashion. In general, the longer the time between transactions the more variance there is in individual house price appreciation. This paper extends this concept to include new dimensions. For instance, houses that appreciate faster than the mean, as estimated by the index for that location, may experience a different variation structure than homes that appreciate slower. This process can be viewed as an asymmetric treatment of the variance of house price appreciation around the estimated index. In addition, the variance of expensive and affordable homes may also be different and time varying. This paper finds evidence that adding the dimensions of price tiers and asymmetry to the variance estimate has merit and does affect the estimated index as well as homeowner equity estimates. Homeowner equity estimates are especially sensitive to these added dimensions because they depend on both the revised index and the estimated variances, which are specific to each dimension considered—time between transaction, asymmetry, and price tier.     

Separating the Age Effect from a Repeat Sales Index: Land and Structure Decomposition

Since real estate is heterogeneous and not all its quality attributes are observable, the repeat sales model pioneered by Bailey et al. (1963) has become one of the standard methods to estimate a constant-quality price index. The model, however, fails to adjust for depreciation, as age and time between sales have an exact linear relationship. This paper proposes a new method to estimate an age-adjusted repeat sales index by decomposing property value into land and structure components. As depreciation is more relevant to the structure than land, the property’s depreciation rate should depend on the relative size of land and structure. The larger the land component, the lower the depreciation rate of the property. This new method is applied to property transactions in Hong Kong and Tokyo. Hong Kong is shown to have a higher depreciation rate based on a fixed structure-to-property value ratio, while the resulting age adjustment is larger in Tokyo because its land value has shrunken over time.     

Nonlinear mean reversion in stock prices

This paper provides new evidence on the time-series predictability of stock market returns by introducing a test of nonlinear mean reversion. The performance of extreme daily returns is evaluated in terms of their power to predict short- and long-horizon returns on various stock market indices and size portfolios. The paper shows that the speed of mean reversion is significantly higher during the large falls of the market. The parameter estimates indicate a negative and significant relation between the monthly portfolio returns and the extreme daily returns observed over the past one to eight months. Specifically, in a quarter in which the minimum daily return is −2% the expected excess return is 37 basis points higher than in a month in which the minimum return is only −1%. This result holds for the value-weighted and equal-weighted stock market indices and for each of the size decile portfolios. The findings are also robust to different sample periods, different indices, and investment horizons.     

Data-snooping biases in tests of financial asset pricing models

Tests of financial asset pricing models may yield misleadinginferences when properties of the data are used to constructthe test statistics. In particular, such tests are often basedon returns to portfolios of common stock, where portfolios areconstructed by sorting on some empirically motivated characteristicof the securities such as market value of equity. Analyticalcalculations, Monte Carlo simulations, and two empirical examplesshow that the effects of this type of data snooping can be substantial.     

Depreciation-Related Capital Gains,Differential Tax Rates,and the Market Value of Real Estate Investment Trusts

We develop a model for valuing U.S. real estate investment trusts (REITs) that considers the tax liability impounded in REITs’ property portfolios. This liability is a function of the portfolio’s accumulated depreciation and is driven by different tax rates applied to individual components of the total gain from property sales. These two components are the capital gain resulting from the sale of property at a price higher than its cost and the gain due to the recapture of depreciation taken during the use of the property. Our measure of value is the REIT’s net asset liquidation value (NALV). The metric of REIT value currently used by analysts is a REIT’s net asset value (NAV), but a REIT’s NAV will always be greater than the NALV and therefore overestimate market value, all else equal. Finally, using observed market prices for REITs, we provide evidence that NALVs give superior estimates of REIT market prices than do NAVs.     

线上销售与未来股票收益

本文通过分析相关上市公司在电商平台的线上销售数据,发现线上销售增长可以预测未来股票收益。根据线上销售增长率构建投资组合可以获得月均1.27%的超额收益,经三因子、五因子模型调整后收益率分别为1.40%和1.35%,并且该超额收益在较长时间内不会逆转。横截面回归结果显示,线上销售增长与未来股票收益显著正相关,并在控制其他市场异象因子后仍然显著。此外,本文还发现线上销售数据的预测能力主要集中在投资者关注有限、线上销售占比高以及套利成本高的公司,其投资价值来源于对公司未来基本面信息的预测能力。进一步研究表明,同时利用线上销售指标和营业收入指标进行投资可以获得更高的超额收益。在考虑业绩预告和业绩快报对线上销售指标预测能力的潜在影响后,结果依然稳健。     

Selectivity,Quality Adjustment and Mean Reversion in the Measurement of House Values*

This paper develops a model of price formation in the housing market which accounts for the non-random selection of those dwellings sold on the market from the stock of existing houses. The model we develop also accounts for changes in the quality of dwellings themselves and tests for mean reversion in individual house prices. The model is applied to a unique body of data representing all dwellings sold in Sweden's largest metropolitan area during the period 1982–1999. The analysis compares house price indices that account for selectivity, quality change and mean reversion with the conventional repeat sales models used to describe the course of metropolitan housing prices. We find that the repeat sales method yields systematically large biased estimates of the value of the housing stock. Our comparison suggests that the more general approach to the estimation of housing prices or housing wealth yields substantially improved estimates of the course of housing prices and housing wealth.     

Are investors really home-biased when investing at home?

Investors who only invest in their domestic market are typically referred to as being home-biased. We refer to firm-level internationalization and call into question whether investing in domestic stock indices actually leads to home bias. We use three measures of firm-level internationalization based on percentages of foreign sales, employees in foreign countries, and foreign tax payments. We aggregate firm-level results to determine the degree of internationalization of German, French, UK and US stock indices. French and UK stock indices exhibit the largest degree of internationalization. The German index provides slightly less internationalization, whereas internationalization of the US index is lowest but nonetheless considerable. This means that investors who invest in their domestic market do not necessarily suffer from home bias. Instead, investing in domestic stock indices more likely prevents investors from a home bias instead of entrapping them to insufficient portfolios.     

Disaggregation of Local Apartment Markets by Unit Type

Recent market segmentation research has begun to delve into the issue of whether traditional property-type categories are sufficiently homogeneous to be modeled as aggregate real estate markets. This article extends the research on rental-property market segmentation by investigating the existence of apartment submarkets determined by unit type. The study finds that one-bedroom, one-bath units; two-bedroom, one-bath units; and two-bedroom, two-bath units function as distinct submarkets differentiated by property features, neighborhood location, and temporal changes in market rent.     

MACROECONOMIC FACTORS, THE APT AND THE UK STOCKMARKET

This paper examines the macroeconomic sources of risk priced in the UK stockmarket between 1983 and 1990 using monthly data on 840 stocks to form both beta-sorted and market value sorted portfolios using the methodology proposed by Chen, Roll and Ross (1986) and Chan, Chen and Hsieh (1985) for the US. We find that several intuitively plausible macroeconomic variables were priced over this period using the beta sorted portfolios and that once these variables are included there is little role for the return on the market. However, when the market value sorted portfolios were used only inflation and a measure of equity market 'expense' relative to gilts was priced; furthermore with the market value sorted portfolios a role for the market return was found.     

Tracking bond indices in an integrated market and credit risk environment

Abstract

The management of credit risky assets requires simulation models that integrate the disparate sources of credit and market risk, and suitable optimization models for scenario analysis. In this paper we integrate Monte Carlo simulation models for credit risk with scenario optimization, and develop a methodology for tracking broadly defined corporate bond indices. Testing of the models shows that the integration of the multiple risk factors improves significantly the performance of tracking models. Good tracking performance can be achieved by optimizing strategic asset allocation among broad classes of corporate bonds. However, extra value is generated with a tactical model that optimizes bond picking decisions as well. It is also shown that adding small corporate bond holdings in portfolios that track government bond indices improves the risk/return characteristics of the portfolios. The empirical results to substantiate the findings of this study are obtained by backtesting the model over a recent 30 month period.     

政府购买医疗保障服务政策下商业健康保险的盈利模式

新医改提出政府向商业健康保险公司购买医疗保障管理服务的思路,为私营医疗保险市场的进一步发展提供了政策支持。但医改方案公布后,除人保健康进入这一市场外,其他公司仍举旗观望,提供该类服务的公司寥寥无几。这一困境出现的原因是保险机构对政府购买经办服务政策下,相关产品组合的盈利模式缺乏认识。对可得的有限数据进行分析,期望总结中...     

Market capitalisation,cross-correlations,the lead/lag structure and microstructure effects in the Indian stock market

The lead/lag relationship between portfolios comprising large and small cap firms is analytically derived in terms of their speeds of adjustment and degrees of thin trading. Using a number of Indian equity index series that differ in their market capitalization characteristics, large cap indices are found to lead small cap indices and to have higher speeds of adjustment towards intrinsic values. However, pure thin trading effects and an interaction effect between thin trading and speeds of adjustment are found to make significant contributions to the lead/lag effect. An empirical analysis of the underlying intrinsic value process using a reduced form model developed in the paper indicates that a small degree of overreaction is present in intrinsic values series.