We examine mortgage lending outcomes in India using a large dataset of approximately 2.5 million approved loans. Using survival analysis and borrower credit history for a subsample, we examine whether the approved-loan sample exhibits the positive-selection pattern predicted by Becker's outcome test. We do not observe superior loan performance or positive selection among approved loans, as a strong taste-based screening model under Becker's outcome test would predict. Instead, approved loans to marginalized caste groups and minority borrowers exhibit higher default risk, a pattern more consistent with underlying borrower characteristics than with stronger screening at approval. We also examine loan pricing and find that these groups face similar, or in some cases lower borrowing costs, after controlling for observables. These findings are consistent with a potential role for affirmative action policies, although selection-based explanations cannot be entirely ruled out. We find that borrowers from marginalized caste groups and religious minorities employed in government occupations exhibit slightly better loan performance, suggesting a positive impact of reservation policies. We implement a battery of robustness checks to address selection issues and examine other potential explanations. Our main findings remain valid throughout these tests.
Urban land in China is state-owned and primarily allocated by the government via a two-stage auction process. Compared with auction mechanisms in other markets and countries, China’s two-stage land auctions have a unique structure in that the first stage is an open-bid survival auction with a fixed deadline. This paper uses bid history data for land auctions in Beijing between 2006 and 2015 to analyze jump bidding behavior, investigating its effects on subsequent bidding and the final sale price achieved. We find that jump bidding increases the likelihood that the next bid is also a jump bid and that it accelerates the bid submission process in the first stage. Furthermore, jump bidding generates two-way effects on the land price. While it prevents lower-value bidders from entering the second stage, it can also trigger a ‘jump war’ between higher-value bidders. These findings document strategic bidding behavior in Beijing’s two-stage land auctions and show how jump bidding can shape competition and prices within this institutional setting.
Given the Chinese real estate industry’s high-leverage business model and government restrictions on secondary equity offerings, debt financing represents the predominant source of funding. In this study, we examine whether real estate firms utilize relationship spending in the form of entertainment and travel costs (ETC), encompassing both bribery and facilitation expenditures, to negotiate more favorable debt terms. Our findings consistently show that higher ETC is associated with lower cost of debt. We further account for voluntary disclosure of ETC with a Heckman selection model, and we mitigate potential endogeneity using a shock-based instrumental variable (IV) design based on an exogenous regulatory shock from China’s anti-corruption campaign. We next show that higher ETC facilitates larger loan approvals and longer maturities. In heterogeneous analyses, we show that the debt cost effects are strongest among state-owned enterprises, private firms with concentrated ownership, and firms with state-owned bank lenders. Taken together, our results suggest that Chinese real estate firms employ ETC as relational capital to lower financing costs and improve loan terms. In so doing, we extend prior evidence on bribery in bank lending by demonstrating how relationship spending operates in a sector with unique institutional features that is central to China’s financial stability.
This study examines how tenant concentration risk influences CEO compensation design in listed equity Real Estate Investment Trusts (REITs). We find that boards of REITs with concentrated tenant bases structure CEO pay packages to offset managerial risk aversion. The relation between tenant concentration and risk-taking incentives awarded to CEOs remains robust to tests addressing reverse causality and endogeneity. The association is stronger when tenants are smaller and less profitable, and weaker when REITs have poor growth opportunities. We also find that higher CEO Vega is followed by greater systematic risk, leverage and portfolios concentration. Overall, the results suggest that REIT boards tailor CEO incentives to tenant-specific operational risk rather than generic market risk alone.
Using a unique dataset of house prices and rents and a difference-in-differences methodology, we provide evidence consistent with a reassessment of earthquake-related risks in Istanbul following the Kahramanmaras Earthquake Sequence on February 6, 2023. We find that post-earthquake increases in both house prices and rents were relatively more subdued in high-risk neighborhoods than in lower-risk neighborhoods, suggesting that seismic safety became more strongly capitalized into housing-market outcomes after the earthquake. Furthermore, houses built before the Turkish Earthquake Code of 2007 are discounted more strongly in high-risk areas relative to newer properties, highlighting the importance of building standards in shaping market responses to seismic risk. Finally, the post-earthquake premium associated with lower-risk locations is less pronounced in neighborhoods with considerably higher socio-economic status. These findings have important policy implications for earthquake-risk disclosure, building-code enforcement, and earthquake-resilience policies in Türkiye.
This paper examines how defined benefit pension funds select and revise performance benchmarks for real assets, including private real estate, listed REITs, infrastructure, commodities, and natural resources. Using data from CEM Benchmarking, we document substantial heterogeneity in benchmark selection across pension funds and asset classes, particularly in less mature private markets. We show that benchmark selection often does not align with underlying portfolio risk. In private real estate, large U.S. pension funds predominantly allocate capital to higher-risk non-core investment strategies while benchmarking performance against lower-risk core real estate indices. Selected private real estate benchmarks also underperform transparent and investable public-market alternatives, implying lower performance hurdles. We further document a growing use of absolute benchmarks in private real asset markets. Benchmark revisions are common and associated with changes in strategic asset allocation. At the same time, benchmark changes coincide with declines in both portfolio and benchmark returns, leaving benchmark-adjusted performance largely unchanged. Overall, our findings suggest that benchmark selection in real asset markets is highly discretionary and often weakly connected to underlying portfolio risk, reducing transparency and complicating delegated performance evaluation.
This study examines the impact of the “Three Red Lines” policy in China, a regulatory initiative aimed at reducing leverage in the real estate sector, on corporate debt risk. Using a difference-in-differences approach, we find that the policy significantly increased bond spreads and realized defaults among real estate developers, particularly for non-state-owned developers (non-SODs). In contrast, state-owned developers (SODs) were unaffected. The divergence appears driven by deteriorating financial and operational performance among non-SODs. The policy also raised financing costs for non-SODs, exacerbating their financial distress. These findings suggest that, although designed to reduce systemic risk, the policy inadvertently heightened debt risk for non-SODs, underscoring the need for more targeted regulatory frameworks.
This study investigates why and with what financial consequences Real Estate Investment Trusts (REITs) engage selectively in Environmental, Social and Governance (ESG) practices across different institutional contexts. Drawing on stakeholder theory and shareholder value perspectives, we argue that REIT managers strategically prioritize social (S) and governance (G) dimensions over environmental (E) initiatives – a strategy we term “selective ESG”. Analyzing a panel of 269 REITs across North America, Europe, and Asia from 2012 to 2022, we find that the financial outcomes of this strategy are divergent and contingent on regional institutional pressures. In North America, selective ESG strategy enhances profitability and market valuations. Conversely, in Europe, where environmental policy is stricter and investor expectations are higher, selective ESG is penalized with lower financial performance. An event study further corroborates this divergence: North America market rewards REITs for enhanced selective ESG practices, while European markets reward REITs for less selective engagements. By demonstrating that the financial return to ESG is not uniform across regions, our research not only uncovers a subtle form of ESG washing but also highlights the critical, yet often overlooked, role of regional institutions in driving corporate sustainability outcomes.
Mortgage forbearance is designed as a safety net, but its effectiveness depends on who uses it and why. This paper asks whether mortgage forbearance during a crisis supports borrowers in financial distress and whether it is taken up primarily by those who need it or by households responding strategically to uncertainty. We develop a behavioral framework that highlights how borrower beliefs, expectations, and perceived costs of participation affect forbearance decisions. These mechanisms are evaluated using a proprietary dataset that links nationally representative administrative mortgage records from the National Mortgage Database (NMDB) with borrower survey responses from the American Survey of Mortgage Borrowers (ASMB). Empirically, we study both entry into and exit from forbearance under the CARES Act and assess the role of contemporaneous borrower expectations, repayment uncertainty, and financial knowledge alongside traditional underwriting characteristics measured at origination. We also analyze how work-related and personal disruptions influence post-forbearance outcomes. The results show that borrower beliefs and information frictions complement standard measures of financial distress in explaining heterogeneous forbearance behavior. In particular, uncertainty about repayment resolution and limited financial understanding are associated with greater difficulty resuming payments when relief ends. These findings suggest that while forbearance can stabilize distressed households during crises, its effectiveness depends on how well programs are understood and perceived. Incorporation of behavioral insights related to borrower expectations, information frictions, and financial understanding may improve the design and communication of future mortgage relief policies.
This study reexamines the impact of out-of-town (OOT) buyers on housing price dynamics by examining whether news sentiment can act as a fundamental driver of these market movements. Using housing news headlines from South Korea, we construct a News Sentiment Index (NSI) via a fine-tuned KR-FinBERT model and incorporate it into a Bayesian VAR framework alongside OOT transaction data. Empirical results show that when NSI is included, OOT transactions no longer Granger-cause housing prices. Indeed, incorporating NSI leads to a substantial reduction in OOT’s price contribution, whereas NSI shocks exhibit a remarkable, immediate impact on price formation. Importantly, this informational influence is heterogeneous between OOT buyers and locals; NSI replaces a substantial share of the own explanatory power of OOT transactions and accounts for nearly half of the influence of OOT activity on local transactions, while having little effect on the dynamics of local transactions. The main analyses suggest that NSI may help alleviate informational frictions faced by geographically distant participants, whereas the outlet-based stale–novelty decomposition indicates that news also conveys genuinely new information, thereby generating more persistent effects on valuations.
This paper examines how substantial rental deposits in housing markets can both reflect and influence future housing prices through their dual role as financial instruments and market signals. Using South Korea’s unique Jeonse system as a case study, we analyze how rental market arrangements combining significant upfront deposits (40–60
This paper examines whether BSADF-identified explosive regimes in global REIT markets predict subsequent tail risk, and whether economically interpretable upward explosive episodes are followed by more severe drawdowns than comparable non-explosive market peaks. Using daily price data for fourteen FTSE REIT indices between 2011 and 2026, we identify explosive regimes with the Backward Supremum Augmented Dickey-Fuller (BSADF) methodology and explicitly distinguish upward from downward explosive episodes. This distinction is important because BSADF detects statistical explosiveness relative to a unit-root benchmark and does not, by itself, establish speculative overvaluation. Post-regime outcomes are evaluated using maximum drawdowns, recovery times, a nearest-neighbor matched control sample of normal peaks based on pre-event return, volatility, and trend conditions, and daily forward-looking tail-risk measures with block-bootstrap inference. The results show substantial heterogeneity across markets and regime types. Upward explosive regimes are not followed by systematically larger drawdowns than matched normal peaks: in the matched sample, mean subsequent drawdown is 12.05
This paper investigates the impact of corporate real estate investment on the financial performance of listed non-real estate firms. Using corporate finance and land transaction data in China, our findings reveal that real estate investments by non-real estate firms are less efficient compared to those made by real estate firms, despite a contagion effect driving real estate investment fever among institutional investors. Exploiting the home purchase restriction policy as an instrument, our baseline results show that, in the short run, real estate investments hinder profitability, increase leverage, and reduce liquidity, negatively affecting the performance of core businesses. While the collateral channel remains significant, these adverse outcomes are primarily driven by the crowding-out channel and snowballing consequences. In the long run, real estate investments lead non-real estate firms to reallocate resources from innovation to land market investments, undermining their growth potential. Our findings provide new insights into the negative contagion effects among institutional investors in the real estate market and have significant implications for the broader economy and government regulations regarding cross-industry investment activities.
Coastal communities are projected to experience 50–100 days of high-tide flooding (HTF) annually by the mid-2040s. While such chronic inundation would pose serious threats to livability, surprisingly little is known about how these risks are priced into coastal housing markets. Leveraging plausibly random variation in HTF occurrences, we find that exposure to HTF depresses both rents and home prices, with the impact on rents being three times larger—suggesting that homebuyers anticipate future recovery in rental values. We show that this optimism likely stems from expectations of future adaptation, particularly by governments.
The impact of institutional investment on residential rental markets is a topic that draws much attention, but for which limited evidence exists. This paper assesses the direct and indirect impact of institutional investment in existing housing stock on residential rents at the property level, using Ireland as a case study. Ireland’s experience of the Global Financial Crisis (GFC) produced a quasi-natural experiment where large portfolios of rental properties became available for purchase, enticing institutional investors into Ireland’s rental market for the first time. By focusing on investment in existing stock, the analysis isolates the impact of institutional investment from any supply effect associated with it being channelled into new rental housing. The study finds that institutional investors increased the level of rents by 4.1 percentage points more than other landlords with comparable properties following purchase.
Airports are crucial transportation facilities, but the impact of their closures on housing markets remains a topic of debate. This paper reveals unexpected negative effects by examining the closure of low‑traffic Beijing Nanyuan Airport. Using a geographic difference‑in‑differences approach, we analyze resold housing data and show that properties in the most-affected group (3–6 km of the airport) experienced a relative price decline after the airport closure. This decline is attributed to the loss of proximity benefits previously provided by the airport. We also document floor-level heterogeneity: compared with properties on higher floors, bottom- and low-floor units exhibit relative price premiums following the airport closure. We prove that these premiums are plausibly driven by closure-induced positive externalities that disproportionately favor lower‑floor residents. These findings challenge the noise-centric narrative, highlighting how airport closures reshape housing markets.
We examine how retail and institutional real estate investors benchmark fund performance across a comprehensive set of performance metrics. Our analysis focuses on a subset of real estate funds for which fund flows, investor type (retail vs. institutional), and risk-adjusted performance are observable. We find that benchmarking practices differ systematically by investor type. Retail investors respond primarily to raw, unadjusted returns, whereas institutional investors are most sensitive to risk-adjusted performance, particularly CAPM alphas estimated using a real estate index as the market benchmark. Although multifactor models most accurately explain historical fund performance, they exert limited influence on the investment decisions or benchmarking practices of either investor group. Ultimately, what drives performance may not be what drives capital.
To promote sustainable urban development, governments worldwide have introduced Green Building Certification (GBC), with China leading the way, yet its corporate consequences remain underexplored. This paper examines how GBC relates to corporate green innovation and financial distress risk. Employing a differences-in-differences design and a sample of Chinese listed firms, we find that GBC is associated with significant increases in both green innovation and financial distress risk. The results survive a battery of robustness checks while accounting for firms’ real estate holdings, indicating that our baseline results do not appear to operate through the collateral mechanism. The association is stronger for firms receiving substantial government environmental subsidies, over-investing in green projects, and led by politically promoted executives, but is weaker for firms with strong CSR performance, suggesting political distortions as the underlying mechanism. Our findings reveal a tension in real estate markets: the very forces that make GBC effective in stimulating green innovation are also associated with a hidden threat, heightened financial distress.
Illicit investment in residential real estate poses persistent challenges for anti–money‑laundering (AML) enforcement due to ownership opacity, fragmented data, and the scarcity of confirmed criminal cases. This study evaluates supervised machine‑learning and neural‑network models to determine their ability to detect properties linked to criminal activity and to identify the most influential predictors of illicit investment. Using a rare‑event dataset and cross‑validated modelling framework, the analysis shows that meaningful patterns can be detected despite extreme class imbalance. Across logistic regression, Random Forest, XGBoost, CART oversampling experiments, and an artificial neural network, consistent predictors—market_value, owner_legal_person, owner_owns_multiple, land_acres, and out_of_state_owner—emerge as central risk indicators. The findings support Rational Choice Theory by illustrating how offenders exploit structural vulnerabilities to maximize utility. Policy implications include property‑level risk scoring, early‑warning systems, and enhanced support for gatekeepers. Machine‑learning approaches show strong potential to strengthen real‑estate AML frameworks.
This paper illustrates how firms with publicly disclosed political connections use subsidiaries to obtain preferential treatment in land markets. While the headquarters of politically connected listed firms pay land prices comparable to those paid by other firms, their subsidiaries receive discounts of 12.1
