Substitutability between Equity REITs and Mortgage REITs

This study extends Seck’s (1996) approach to investigate the degree of substitutability between equity real estate investment trusts (EREITs) and mortgage real estate investment trusts (MREITs). The variance ratio test and the variance decomposition of forecast errors yield results indicating the existence of informational commonality between EREITs and MREITs. The findings indicate that the two types of REITs are substitutable. A direct implication is that investors who believe they have superior forecasting ability will be indifferent to invest in either type of REIT. Another implication is that REITs can be treated as a single asset class in constructing a diversified multi-asset portfolio.

Three essays on real estate research

This dissertation consists of essays relating to the three important real estate research topics: spatial statistics, mortgages, and real estate investment trusts (REITs). In the first essay, “Spatial Distribution of Retail Sales,” we apply retail gravity models to examine the spatial distribution of retail sales for a retail chain in the Houston market. Unlike previous empirical studies, our study models both the spatial dependencies among both consumers and retailers. Our results show both the spatial dependencies have significant impacts on the estimates of parameters in retail gravity models. Contrary to the suggestions of Guitschi (1981) as well as Eppli and Shilling (1996), our results show the importance of the distance parameter in retail gravity models may be understated about 68%. Thus, previous studies may overestimate the deterministic extent of trade areas and, thus understate the importance of good locations. The second essay, “Local Housing Prices and Mortgage Refinancing in US Cities,” has implications the valuation of mortgages, in particular mortgage-backed securities (MBS). This essay provides additional evidence that house prices significantly impact aggregate refinancing and thus directly impact mortgage termination. Previous studies typically focus on the effect of negative appreciation on refinancing. In contrast to previous studies, this essay provides empirical evidence that a positive house price appreciation may motivate borrowers to refinance for capital structure-based or consumption reasons. The third essay, “Monitoring and Dividend Policy for REITs under Asymmetric Information,” examines the interaction between monitoring and two competing explanations for REIT dividend policy under asymmetric information. Specifically, the REIT empirical literature offers two competing theories for the level of dividend payouts under asymmetric information. Wang, Erickson, and Gau (1993) confirm the agency-cost theories. Bradley, Capozza, and Seguin (1998) support the signaling explanations dominating agency cost explanations. In this essay, we demonstrate that by introducing proxies for taxable income and monitoring, we provide evidence that supports agency cost explanations for ineffectively monitored REITs. Furthermore, in contrast to Bradley, Capozza, and Seguin (1998), we show agency cost explanations dominate signaling explanations for these REITs

Low-frequency volatility of real estate securities in relation to macroeconomic risk

Real estate securities have a number of distinct characteristics that differentiate them from stocks generally. Key amongst them is that under-pinning the firms are both real as well as investment assets. The connections between the underlying macro-economy and listed real estate firms is therefore clearly demonstrated and of heightened importance. To consider the linkages with the underlying macro-economic fundamentals we extract the ‘low-frequency’ volatility component from aggregate volatility shocks in 11 international markets over the 1990-2014 period. This is achieved using Engle and Rangel’s (2008) Spline-Generalized Autoregressive Conditional Heteroskedasticity (Spline-GARCH) model. The estimated low-frequency volatility is then examined together with low-frequency macro data in a fixed-effect pooled regression framework. The analysis reveals that the low-frequency volatility of real estate securities has strong and positive association with most of the macroeconomic risk proxies examined. These include interest rates, inflation, GDP and foreign exchange rates

Using Document Indexers for Faceted Search in Dataspaces

Efficient information retrieval is essential to enrich user experience when searching for documents in dataspaces. With the continued growth in the volume and complexity of documents, the efficient information retrieval for searches has become increasingly challenging. To improve users’ search experience, faceted search combines direct keyword search methods with faceted browsing using a predefined set of categories (facets). This paper studies a faceted search approach that integrates dynamic facets generation with search. To further enhance the faceted search, alternative indexers based on pre-defined ontology for data repositories within dataspaces are evaluated in terms of execution time and data size. Experimental results suggest that combining the proposed faceted search with appropriate indexers improves search performance enhancing user experience

Chasing housing prices: Working paper series--06-08

If a person or organization is planning to buy real estate in the future but is unable or unwilling to buy it now, how can they best "hedge" this purchase? In what class of asset should they invest their money until they are ready to purchase the real estate? This paper uses Monte Carlo simulation and bootstrap techniques to help answer these questions. We find that the best "purchase early" hedge for both residential and commercial real estate is small value stocks. Small value stocks would be the most likely to provide returns at least as good as real estate and they would be least likely to suffer losses relative to real estate. The effectiveness of the hedge increases the longer the time horizon of the investor. Large value stocks and equity REITs are also quite good but not as good as small value stocks. Other asset classes are not nearly as effective. The least effective asset class is T-Bills

Chasing Housing Prices?

If a person or organization is planning to buy real estate in the future but is unable or unwilling to buy it now, how can they best “hedge” this purchase? In what class of asset should they invest their money until they are ready to purchase the real estate? This paper uses Monte Carlo simulation and bootstrap techniques to investigate the effectiveness of using traditional asset classes in managing the long-term risks associated with the future purchase of real estate. We find that the best “purchase early” hedge for both residential and commercial real estate is small value stocks. Small value stocks would be the most likely to provide returns at least as good as real estate and they would be least likely to suffer losses relative to real estate. The effectiveness of the hedge increases the longer the time horizon of the investor. Large value stocks and equity REIT’s are also quite good but not as good as small value stocks. Other asset classes are not nearly as effective. The least effective asset class is T-Bills

Do European real estate stocks hedge inflation? Evidence from developed and emerging markets

This study examines the inflation-hedging properties of European real estate stocks in developed and emerging markets over 1990 to 2011. The Fama and Schwert model and a dynamic ordinary least squares (DOLS) regression were employed to study the inflation-hedging characteristics of European real estate stocks over the short run and long run. The empirical results show little inflationhedging ability of European real estate stocks over the short run. Over the long run, developed real estate stocks provide a positive inflation hedge against expected inflation, while no similar evidence is found in the emerging markets. The findings suggest that the inflation-hedging properties of real estate stocks are related to the institutional involvement in the real estate stock markets. The finding could have profound implications to institutional investors

Price discovery and volatility transmission in Australian REIT cash and futures markets

This study examines the price discovery function and volatility spillovers in australian real estate investment trust (A-REIT) index futures and also investigates the effects of the global fi- nancial crisis (gfc) on these two features. as opposed to the general understanding of the relationship between the cash and the futures markets, the current study finds that the A-REIT cash market led the a-reIt futures market in price discovery and volatility transmission processes before the gfc. However, during the GFC, the two markets interacted bilaterally in terms of information flow, i.e., in- formation flowed in both directions. Furthermore, after the GFC, the futures market followed the cash market again, but less closely. These findings have broad implications for investors in property assets

Circulating vaccine-derived poliovirus: a menace to the end game of polio eradication

10.1186/s12992-020-00594-zGlobalization and Health1616

Inferring genetic interactions via a nonlinear model and an optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.</p> <p>Results</p> <p>An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in <it>S. cerevisiae</it>, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.</p> <p>Conclusions</p> <p>GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.</p