This paper examines the implications of the maturity structure of mortgage contracts for the effects of the cost of mortgage on house prices. Theoretical results indicate that economies with larger shares of variable interest rate mortgage contracts present faster responses of house prices to changes in the cost of mortgage, as well as higher interest rate sensitivities of cost of mortgage and house prices. Lower mortgage rate levels, higher loan-to-value ratio, and higher effects of cost of mortgage on house prices also increase these sensitivities. Empirical results validate the theoretical ones. Local projections estimated for the US, UK, Sweden, Canada, Finland and France show negative and significant responses of house prices to shocks on the cost of mortgage, with responses being faster in economies with larger shares of variable interest rate mortgage contracts. Estimates of the interest rate sensitivity of the cost of a mortgage are also in line with theory and increase as the mortgage rate declines. This sensitivity is attenuated when the central bank shifts its focus of monetary policy towards longer-term rates or when the effective lower bound is perceived to be lower.

This paper examines the role of real estate in U.S. pension portfolios, considering various funding ratios and liability durations. It also investigates the composition of the real estate allocation across sectors and market types. We develop a non-parametric block bootstrap surplus optimization that incorporates modeled liabilities that account for changes in pension beneficiaries’ wages, changes in the discount rate curve, and mortality. When considering the liabilities of a pension fund, the optimal real estate allocation is found to be substantially lower than in the asset-only case. Plans that are relatively close to a zero surplus exhibit the highest allocations. When low-risk portfolios are considered, plans that have a longer duration have a higher allocation to real estate than plans with a shorter duration. For a high-risk strategy, the highest real estate allocations are for plans that have moderate liability durations. Overall, our results support institutional investors’ preferences for gateway markets. Concerning sectoral allocations, industrial properties dominate the real estate allocation.

This research provides insight into the connectivity between transportation and oil futures, and U.S. industrial property values. Traditional discussion of real estate values with regards to transportation has generally been in the domain of residential real estate; we examine this relationship in the context of industrial real estate. Short- and long- term models examining private and public industrial real estate values inclusive of fundamental transportation variables, futures prices, and a transportation financial index, along with economic control variables are examined. Results suggest connectivity between sectors, with truck shipments and the Dow Jones Transportation Index positively related to industrial real estate prices at national and regional levels. Regional results generally align with national findings, though some heterogeneity is observed. In the short run, railroad carloadings and truck tonnage explain significant forecast error variance in private industrial property markets, while the Dow Jones Transportation Index does the same for publicly traded industrial REITs.

This study investigates the data analysis capabilities of GPT-4o in real estate portfolio selection by integrating predictive modeling, model evaluation, and investment decision-making into a fully autonomous AI-driven framework. Unlike the earliest large language models (LLMs) that primarily process textual data or recent LLMs such as OpenAI’s o1 and DeepSeek’s R1, which are designed for complex reasoning, GPT-4o actively executes code and conducts quantitative analysis using the Code Interpreter tool. Leveraging a dataset of Zillow home price data and several predictive factors, the AI-generated portfolios consistently outperform various benchmarks in our out-of-sample backtest. Further, we find that data obfuscation—removing city names, states, and dates—reduces geographic diversification and produces lower Sharpe ratios than the unobfuscated portfolios. Overall, our findings highlight the potential of generative AI in advancing data-driven portfolio management.

Obtaining insight in the determinants of investment returns and their volatility has a long tradition in asset pricing modeling. For real estate investment trusts (REITs), the results have been mixed, with no clear consensus emerging. We apply the Bayesian model averaging method to let the data select the most robust indicators. Using a comprehensive sample of U.S. office, industrial, and healthcare REITs, we identify the most robust drivers of their excess returns and volatility for the period 2001 to 2024. We find that returns are mostly driven by domestic financial markets indicators, and the volatility of the returns is driven mainly by REIT-specific indicators. By analyzing three subperiods, we find that the Global Financial Crisis (GFC) had a disruptive impact on both the returns and volatilities, as the number of robust variables in the post-GFC period is roughly half that of the pre-GFC period, suggesting a shakeout due to the economic shock that led to more consolidation within the sector. In the COVID-19 period, the drivers show resilience because these resemble the pre-GFC period more, which suggests that the GFC had a more disruptive impact than the COVID-19 crisis.

This study identifies housing price bubbles in 10 developed countries from 1996 to 2022, employing both traditional and novel methods. Unlike most research, which focuses on statistical tests for explosive behavior in the price-to-rent ratio, we estimate the non-fundamental component of housing prices and test for exuberance in this component. Our findings reveal significant differences between the methods. The generalized supremum augmented Dickey-Fuller statistic consistently shows higher values for all countries when using the price-to-rent ratio, indicating a higher probability of identifying bubbles with this ratio compared to the non-fundamental component. Additionally, more bubbles are detected using the price-to-rent ratio, particularly in the latter part of the sample period, and the estimated duration of these bubbles is longer. These results suggest that relying solely on the price-to-rent ratio may lead to an overestimation of bubble occurrences and durations. For more accurate policy decision-making, it is crucial to analyze the non-fundamental component of housing prices and compare its behavior with the price-to-rent ratio, reducing false positives and enhancing our understanding of housing market dynamics.

Home purchasing is a multifaceted decision representing the largest investment an individual can make, carrying significant financial risk. Real estate sellers or agents often provide information that creates a gap between buyers’ expectations and the actual product, leading to post-purchase regret. This study aims to identify factors affecting home-buying and regret levels. Indicators were extracted and categorized as locational and marketing factors that influence consumer behavior and help them make informed decisions. Data from 450 households in Islamabad, Pakistan, were collected and analyzed using ordinal regression analysis. The results revealed that proximity to work, distance from the city center, educational institutions, highways, public transport, healthcare facilities, and parks, concerns related to safety and security, drainage system availability, brand name/developer reputation, and marketing efforts through celebrities, TV channels, and the internet, were influential in reducing regret levels associated with home-buying decisions. This study helps policymakers, developers, and individuals make the impacting factors mandatory in regulatory approvals and amend land use regulations for sustainable housing. This can ultimately reduce the likelihood of post-purchase regret.

This paper combines the literature of two schools of studies. It clarifies and introduces a conceptual framework with consumer confidence index and commercial real estate return. The research investigates whether fundamentals or consumer sentiment is more helpful in explaining commercial real estate returns. It proposes that consumer sentiment is exogenous in the conceptual framework. Consumer confidence index is decomposed into two components: fundamentals, as signaled by macroeconomic indicators, and the residual part, representing consumer sentiment. Using data in New Zealand markets, it is found that commercial real estate total return and capital return are more explained by consumer sentiment than by fundamentals. On the contrary, commercial real estate income return is more explained by fundamentals. Consumer sentiment imposes sustained effect on capital return and total return. Findings imply that valuers incorporate market sentiment into their valuation process. On top of that, investors rely on market sentiment during the transaction process. Industrial and Bulk Retail sectors enjoy positive impacts of COVID on capital return. Results imply potential diversification benefits across different commercial real estate sectors during market shocks. The study provides additional insight into consumer confidence and consumer sentiment and its intriguing relationship with commercial real estate return.

This study examines the performance of U.S. real estate funds of both exchange-traded funds (ETFs) and mutual funds, including passive ETFs, active ETFs, retail-class mutual funds, institutional-class mutual funds, and index mutual funds. The active ETF portfolio stands out with the highest returns and Sharpe ratio and consistently higher alphas in the factor models. It exhibits more prominent performance in downtrend markets and in recent years, whereas its peer retail-class mutual fund portfolio has consistently shown inferior performance. The passive ETF and the index mutual fund portfolios have never surpassed the performance of the active ETF portfolio. Neither choosing the cheap index strategy nor paying for high-cost mutual fund managers proves to be the optimal option for investors. The outperforming portfolio is found to be active ETFs, which sheds light on the continuing surge in their fund flows.