In markets dominated by pre-sales, which is a structural feature of housing provision in emerging economies, constructing price indices is limited by the lack of resale history. Addressing this statistical blind spot, this study proposes a novel empirical strategy which exploitspurchase contract cancellations to identify repeat-sales pairs. Utilizing a dataset of 25,049 pairs from Santiago (2013-2024)in Chile,this approach mitigates depreciation bias by comparing the same never-occupied unit over time. Results from geometric and arithmetic estimators reveal three key findings: (1) a distinct price hierarchy, where capital appreciation disproportionately favorsentry-levelhousingand investment segments; (2) the index serves as a leading indicator, which anticipated the post-2021 market correction ahead of official administrative records; and (3) a 27% nominal loss rate in subsequent placements, which exposes the magnitude of price adjustments consistent with liquidity frictions. These findings validate cancellations not merely as administrative voids, but as real-time signals of financial distress, thus offeringa replicable monitoring tool for global jurisdictions reliant on the pre-sale leverage model.
The Taiwanese government has long regarded the real estate sector a a key driver of economic growth because of its perceived multiplie effects. However, rising housing prices and worsening affordability hav raised concerns about whether the housing market contribute meaningfully to long-term economic development. This study re examines the relationship between housing prices and the macroeconomy in Taiwan by using quarterly data from 2006Q1 t 2025Q3 within a vector error correction model framework. The result confirm the existence of long-run cointegration among housing prices gross domestic product (GDP), construction activity, population, an inflation. Housing prices are found to be weakly exogenous, while GDF construction activity, population, and inflation adjust to restor equilibrium following shocks. In the long run, housing prices ar influenced by supply-side conditions, demand pressures, financia factors, and structural shocks such as the global financial crisis an COVID-19 pandemic. In the short run, housing price shocks exert limite effects on broader economic performance. Overall, the findings sugges that although the real estate sector is closely linked to macroeconomi conditions, its contribution to sustained economic growth is limited.
This study examines how board structure, director characteristics, and firm fundamentals jointly influence the frequency of meetings of real estate investment trust (REITs) boards from 2010 to 2022. We find that board activity is largely reactive to firm conditions rather than a proactive governance mechanism. Across the full sample, the boards meet more frequently following a weaker performance, higher leverage, and greater uncertainty, which is consistent with meetings that serveas monitoring responses to emerging risks. We further document that firms with weaker growth prospects shift board attention toward monitoring, while firms with stronger growth opportunities engage boards more in advisory and strategic deliberations. In addition, there is substantial heterogeneity across institutional environments. In large, highly visible S&P 500 REITs, board size and independence are associated with greater engagement across monitoring and advisory functions, thus suggesting that governance structures operate more effectively under strong market scrutiny and transparent information environments. In contrast, smaller non-S&P 500 REITs exhibit more episodic governance patterns in which meeting activity responds broadly to performance shocksand financial risk, and where structural governance attributes do not consistently translate into board engagement. Additional analyses show that board meetings respond to prior firm conditions but do not predict future operating performance, thus indicating that meeting frequency signals governance response rather than governance effectiveness
This paper presents an improved model for forecasting rental property values in large metropolitan areas by incorporating features derived from the spatial distribution of urban lighting. The study uses rental housing data from Houston and Los Angeles (USA) together with high-resolution nighttime satellite imagery to generate additional explanatory variables. Light clusters are identified from satellite images and processed to determine their geographic location and spatial relationships. The clusters are georeferenced by using the Quantum Geographic Information System,thusenabling integration with other spatial datasets and improving modelling accuracy. The paper describes the methodology for feature extraction, spatial clustering, and integration into machine learning workflows. A Light Gradient-Boosting Machine predictive model is developed and compared with baseline models. The experimental results show that the proposed approach reduces the mean squared error by 11.8% for Houston and 9.37% for Los Angeles relative to conventional models. The findings demonstrate the usefulness of nighttime illumination features for capturing socio-economic and spatial patterns relevant to urban rental markets. The proposed methodology highlights the potential of combining geospatial data and machine learning techniques to improve automated valuation models and support urban analytics and smart city planning.
The built environment accounts for a substantial share of urban greenhouse gas emissions, and a growing number of U.S. cities have responded by enacting mandatory building performance standards (BPS) that impose energy and emissions targets on commercial and multifamily properties. Multifamily housing, which represents roughly one-third of the U.S. housing stock and an even larger share of building-sector emissions in major cities, sits at the center of this regulatory shift. However, the BPS landscape is highly fragmented across jurisdictions in terms of metrics, targets, and enforcement, thus creating compliance complexity for developers, owners, and asset managers. This report draws on interviews with executives ofrepresentative multifamily companies across the United States to document industry perceptions and experiences of municipal energy efficiency mandates. It summarizes the principal compliance challenges, cost and operational implications, and strategic considerations that shapehow the multifamily sector is navigating the transition to stricter performance standards.
