Using a sample of real estate (RE) firm-year observations from 30 countries, we show that greater firm-level climate change exposure (FCCE) is associated with lower stock liquidity. Among the components of FCCE, opportunity-related exposure shows the strongest negative association with liquidity. By contrast, physical risk exposure is positively associated with liquidity after controlling for Country × Year and Sub-industry fixed effects. We further find that national climate governance moderates the relationship between FCCE and liquidity. More stringent environmental policies and long-term climate commitments, such as net-zero targets, mitigate the adverse effect of FCCE on liquidity whereas the evidence for building-sector regulations is weaker. At the firm level, environmental performance also weakens this relationship. In addition, the negative association between FCCE and liquidity is less pronounced in more carbon-intensive sectoral environments as measured by both GHG and CO _2 emissions. This result is consistent with the view that climate risk is already more fully priced in high-emission settings which reduces the additional uncertainty associated with FCCE. Overall, our results show that FCCE, corporate environmental performance and national climate governance jointly shape the stock liquidity of RE firms.
In 2022 the U.S. housing market froze: mortgage rates doubled in under a year, transactions collapsed, and homes sat unsold, yet prices barely moved. Housing adjusts first through liquidity, not price, and the freeze makes a basic question pressing. What does a neighborhood’s liquidity foretell about its future returns? Treating roughly 3,000 U.S. counties and 854 metropolitan areas as housing assets, and measuring liquidity from public listing flows such as days-on-market over July 2016 through May 2026, we find that time-on-market is a strong forward-looking signal in the cross-section of house-price returns. The most illiquid neighborhoods go on to appreciate about three percentage points per year less than the most liquid, the opposite of the illiquidity premium that compensates investors in financial markets. The predictability is genuine out of sample: adding liquidity to a forecasting model that already contains momentum, reversal, volatility, size, and value raises the cross-sectional out-of-sample R^2 by 4.5 percentage points, and the gain survives on transaction-based repeat-sales returns. Its timing is distinctive: the predictability builds over the first year and fades within two to three, a term structure consistent with a bounded, self-resolving adjustment in a soft market whose prices have not yet caught up with demand, and hard to reconcile with either a compensated risk premium or a fixed trading cost, neither of which would dissipate. A genuine price-impact proxy carries no predictive power at all, and the relation survives controls for inventory levels, inventory accumulation, and new supply. The signal is systematic rather than idiosyncratic, and it is organized in space, as a neighbor’s freeze forecasts a county’s own subsequent decline. Because every result holds on transaction-based repeat-sales indices, none is an artifact of index smoothing. Neighborhood liquidity is thus a measured, spatially transmissible leading indicator of house values: a map of where markets are freezing today is an early-warning map of where prices will fall tomorrow.
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.
