The Journal of Real Estate Finance and Economics

We examine the extent to which the quality of innovation created in different locations is related to subsequent changes in house prices in these metropolitan areas. Cities that foster a healthy quality of innovation are likely the home of many successful entrepreneurs and firms that provide high paying jobs. We hypothesize that the relation between innovation and changes in house prices is positive because, all else equal, locations with higher quality of innovation should not only be more desirable places to live, but also support higher rate of wealth and income growth, which allow for higher house price appreciation. We find results consistent with our hypothesis: there is a statistically and economically significant positive relation between innovation quality and subsequent house price appreciation. We find that this association runs from innovation quality to house price appreciation but not the reverse, and that the effect is stronger in areas with inelastic land supply.

This paper studies empirically the capital structure of Turkish REITs as they offer unique and so far untested angles. They do not have to pay out dividends, yet enjoy the exemption from paying corporate taxes since their legal foundation in 1998. Several financial meltdowns occurred in the last three decades, keeping investors with a doubt about Turkey’s financial and political stability. The last meltdown in 2001 is part of the sample period. Findings show that Turkish REITs employ little long-term debt in their capital structure. The legal requirement that a leader entrepreneur be present with a minimum equity position of 25% introduces the agency problem between the majority and minority owners. The leader entrepreneurs, as non-taxable institutional investors, appear to dictate Turkish REITs’ dividend and debt policies and deplete REITs’ dividends, causing them to go to the long-term debt market. The financial meltdown of 2001 exerts negative short-term and positive long-term influence on the debt ratios while inflation’s effect is negative. Firm size, REITs’ engagement in development and stock market development influence debt ratios positively; tangibility and a few firm, ownership, and country-specific determinants appear to have either mixed or no influence on Turkish REITs’ debt policies.

We investigate model risk in pricing no-negative-equity guarantees (NNEGs) with the aim of identifying the housing risks involved in equity-release products. To analyze the regional and local effect in the house price modeling, we evaluate different models using the house price index (HPI) based on the cities of London, Manchester and Coventry and the UK nationwide HPI respectively. The ARMA-GARCH jump model that can capture the characteristics of jump persistence, autocorrelation and volatility clustering are proposed according to the model fittings. To investigate the model risk on the cost of NNEGs, we then derive the risk-neutral valuation framework using the conditional Esscher transform technique (Bühlmann et al. 1996). Our numerical analyses reveal that the housing model risk affects the costs of NNEGs significantly. In addition, the cost of NNEGs is significantly different for different cities due to localized effect. Therefore, the basis risk is large enough to matter when pricing NNEGs.

This paper investigates the extent of regional integration (or, conversely, segmentation) in US home values. In contrast to some previous studies, we examine the degree of integration in the US with a data set which runs into 2012 and thus captures the latest period of bubble and bust, and employing a recently developed set of tools which yield estimates which are 1) time-varying, and 2) account for differences not just in correlation but also in amplitude between different housing markets. Our results indicate that contrary to some previous findings, overall integration in the US was falling, not rising over the early years of the bubble (2001–05). This lends some credence to the “lots of local bubbles” conjecture of Greenspan that the early stages of the bubble reflected froth in some individual markets, rather than a large underlying national bubble. However, the late stages of the bubble exhibit a very sharp rise in integration, so the later bubble and subsequent bust likely reflected national (or global) factors. Finally we find substantial variation across regions in terms of how integrated they tend to be. This comports with previous findings on the low level of integration of regional income in the US, and the ability of home values to maintain substantial segmentation makes the use of housing in monetary policy problematic.

We investigate urban spatial development assuming that landowners irreversibly develop property in an uncertain environment. Unlike the standard monocentric city model, we assume that bid rents for houses are not monotonically decreasing with the distance from the central business district (CBD) because there exist subcenters that are subsidiary to the CBD. As a result, land is initially developed outward from the CBD. Leapfrog development may happen, i.e., distant land from the CBD may be developed prior to nearby land; however, land that is developed later will be more densely developed because it is developed at a better state of nature. We further find that the development patterns of at least four large cities are consistent with that predicted by our model.

This article analyzes the problem of optimal space allocation in shopping centers in the presence of inter-store externalities. In the model, a given store's sales depend on its own space as well as on the space allocated to other stores in the center. The given stores' own sales rise as other stores grow in size because the shopping center is then more attractive to customers. Taking this externality into account, the developer allocates space to the various stores to maximize the shopping center's profit, which equals total rent minus operating costs. The solution to this problem is analyzed under a number of different behavioral assumptions.

Many papers have recently pointed out that institutional investors allocate only a very small fraction of their portfolio to real estate, much smaller than theory would dictate. This raises the question, are institutional investors underinvested in real estate equities? Or do we simply have the wrong priors? This paper is an attempt to provide some new insights into this asset allocation paradox. The key conclusions of the paper are several: First, unlike other assets, it would appear that real estate, and real estate diversification, pays off at the very time when the benefits are most needed, that is, when consumption growth opportunities are low. Second, real estate returns are predictable. In fact, the amount of predictability in real estate returns appears to be about the same as in stock returns. Third, real estate performs well in an asset-liability framework. Fourth, the chance of experiencing a large loss on real estate over a long horizon is quite small. We also report here that private sector commercial real estate investments represent between 6 and 12 percent of investable wealth in the United States. Thus, it follows (if one believes the capital asset pricing model) that if institutional investors were to invest more in real estate (up to 12 percent of their assets), they should be able to eliminate nonmarket or unique risk. All of this leaves us a bit dumbfounded as to why institutional investors hold only between 2 and 3 percent of their assets in real estate.

We study the potential model instability problem with respect to mortgage default risk and examine to what extent it helps explain the default shock during the recent crisis. We find that econometric default risk models based on historical data can be unstable over time. Due to temporal shifts in the parameters, default prediction of the 2006 vintage subprime loans based on hazard and Logit models estimated with 2003 vintage loan data can generate over 40% fewer defaults than the actual number, assuming perfect forecast of house price change. We also find that the combined impact of parameter instability and bad forecast of HPI enlarges the under-prediction of default rate but the marginal impact of parameter instability is larger than that of bad HPI forecast. Our findings have important implications regarding model limitations and risk, model improvements, economic capital, and regulatory reform.