Various forms of housing price and rent control policies are implemented in many countries, and finding their impacts is an important issue. Over 2019-2023, the South Korean government announced a policy to put a ceiling on housing prices in some regions of Seoul, and then subsequently implemented, strengthened, weakened, and finally abolished the policy. This is a rather complicated scenario for a policy, and the goal of this paper is to assess the effects of the policy and its changes with difference in differences (DD). We establish a detailed DD-analysis protocol, employing diverse forms of DD. Applying the protocol where a systematic difference in the untreated outcome trajectories of the treated and control groups is allowed, we assess the policy impacts. We find that, despite the active involvement of the government in the housing market, the overall effect is about a 4-5% decline in housing prices in Seoul.

Did pandemic relief fraud inflate house prices?

Government building relocations and house prices: Evidence from Shanghai, China

Building value: Hedonic pricing analysis of energy performance ratings and house prices in Dublin, Ireland

Multi-level contributions of blue-green spaces to social capital development among older adults: An empirical study in Guangzhou

How can we capture the value of rail transit to improve spatial equity across income groups?

While existing studies have identified various internal and external factors influencing policy adoption and diffusion, the impact of these factors on social policies with complex and hybrid attributes remains unclear. In China, public housing policies serve dual functions: both redistributive and developmental, aiming to enhance social welfare while also driving economic development. This study conducts an event-history analysis of 264 prefecture-level cities between 2010 and 2020 to examine the internal and external factors shaping the adoption of inclusive public rental housing (PRH) policies for urban migrants. The findings indicate that economic interests linked to industrial development tend to promote the inclusion of non-local residents, while those associated with land and housing commodification significantly hinder such inclusion. While rising housing prices raise social welfare concerns that could encourage migrant inclusion, particularly in inland regions, these concerns are often overshadowed by local governments' land-based economic interests. Externally, both vertical and horizontal diffusion mechanisms contribute to the adoption of inclusive PRH policies. Vertical influences at the provincial level manifest primarily through performance-based assessment directives, while horizontal influences are predominantly driven by general competition rather than purely economic performance competition. This study provides insights into housing welfare expansion in a developing and transitional economy, moving beyond the context of high-income Western countries. • We examine internal and external factors shaping inclusive PRH policy adoption in Chinese cities. • Internally, social welfare interests are secondary to industrial and land-based economic interests in shaping policy adoption. • Externally, horizontal competition operates alongside provincial directives in driving policy adoption. • Vertical influence at the provincial level functions predominantly through performance-based assessment directives.

Impact on house prices of an energy renovation obligation for homebuyers

ABSTRACT We evaluate alternative forecasting strategies for the Korean housing market by comparing a DSGE model with several machine-learning methods in an identical empirical setting. Ridge regression yields lower MAE, MAPE, and RMSE than the DSGE model, while being considerably easier to implement. Simpler penalized linear models (LASSO and Ridge) outperform more complex machine-learning methods, including MLP and XGBoost. This pattern is consistent with the tendency of high-capacity models to overfit limited training data, which lowers their accuracy on out-of-sample data. Variable-importance rankings from the machine-learning models align closely with the structural drivers emphasized in the DSGE framework, suggesting that these data-driven methods can retain economic interpretability.

Purpose With the boom of shale oil and gas in the USA as the context, the quick expansion of pipeline networks has garnered public concern regarding the economic consequences of pipeline failures. Design/methodology/approach The survey examined 426 gas pipeline accidents in the USA from 2010 to 2020 and examined their impact on housing prices. Findings It was found that only a very serious accident on the ground pipeline (for example, an explosion, a fire and someone else was injured, which we called a “high exposure events”), reduce the price of a neighboring house, and other types of accidents did not cause any major changes in housing prices. Further analysis also showed that high exposure events would reduce housing prices within 1 billion meters of the pipeline by 8.2%, and this poor effect lasted an average of about 8 years. Originality/value In addition, we found that at the beginning of the business, the volume of the real estate transaction fell slightly, which shows that the power side of demand responds quickly to such things. Compared to the apparent impact of the pipeline accident, the impact of the pipeline installation on housing prices is not great, and statistically little meaning. The study also showed that different types of things are very different, so it is impossible to summarize all the situations with the results of the same thing.

The relationship between intrinsic capacity and functional ability - identifying key environmental features to support healthy ageing.

This paper uses time-series data from 1996Q1-2021Q4 to examine the determinants of non-performing loans (NPL) in Namibia’s banking industry and test for causality between NPL and its determinants. To accomplish this, the Autoregressive Distributive Lag (ARDL) and the Vector Autoregressive (VAR) pairwise Granger causality modelling approaches are employed. The findings reveal that in Namibia, NPL is influenced by a host of factors, including its own past values, output gap, unemployment rate, housing prices, return on assets, return on equity, lending behaviour, loan-to-deposit ratio, loan growth, narrow money supply, broad money supply, net foreign assets, repo rate, interest spread, deposit rates, private sector credit extension, oil prices, COVID-19 pandemic crisis, stock market prices, regulatory quality, government effectiveness, and the rule of law. The Granger causality test results indicate strong unidirectional causality running from past values of NPL, unemployment, housing prices, capital adequacy ratio, loan growth, and oil prices to NPL. Additionally, a bidirectional causal relationship exists between the repo rate, lending rate, and NPL. The policy implications emanating from this study need to be addressed in order to ensure the stability of the country’s financial system.

China’s urban size distribution has increasingly shifted toward concentration in large cities amid global urbanization and the restructuring of urban development patterns. This trend has intensified governance and spatial pressures in major cities while exposing weaker growth momentum in small and medium-sized cities and reducing overall urban system coordination. Existing studies mainly explain this pattern through market forces such as agglomeration economies, housing prices, and migration, while others examine the consequences of local land practices from the perspectives of land finance, local competition, and institutional change. However, there is still no systematic explanation of why centrally imposed aggregate land constraints, operating through heterogeneous local land allocation, generate uneven urban outcomes. Against the background of the 2004 strict land management reform, this paper develops a theory-oriented conceptual framework linking central land constraints, local land allocation, and urban size structure. It clarifies how uniform central constraints may be translated into uneven urban outcomes through differentiated local land-allocation practices. Local land allocation is identified as the key transmission mechanism through which development opportunities are reshaped across cities and, under specific institutional conditions, the upper tiers of the urban hierarchy are reinforced. This paper therefore offers a bounded explanation of how central–local land governance shapes China’s urban size structure, while also underscoring the relevance of land governance to more balanced, resource-efficient, and sustainable urban development.

Accurate evaluation of house valuation is crucial. Misestimation of house prices creates serious consequences for a variety of stakeholders. House prices can be modeled as a function of their constituent attributes. House prices are fuzzy due to negotiation and unpredictable market conditions. This study aims to develop rules to predict triangular fuzzy numbers of price for a given new house by implementing locally weighted KNN-based fuzzy regression, and to compare its performance with possibilistic fuzzy regression. The dataset used is the house valuation dataset. Data is examined using the modified Cheng and Lee k-nearest neighbor fuzzy regression and Tanaka’s possibilistic fuzzy regression. It is found that the modified Cheng and Lee k-nearest neighbor fuzzy regression outperforms possibilistic fuzzy regression in predicting the triangular fuzzy number of the house prices. The best performance is achieved when data is trained using the modified Cheng and Lee k-nearest neighbor fuzzy regression using: = 29 nearest neighbors, Minkowski distance with exponent parameter = 1.6 and an unequal weighting scheme with r = 1.

Geographically weighted regression model with a spatially autoregressive term of the response variable (abbreviated to GWR-SAR model) is a powerful tool to tackle spatial nonstationarity in spatial autocorrelation and regression relationships. Generally, its estimation uses the weighted least squares. However, these weights are usually determined by the predetermined function in simple forms, which may compromise estimation accuracy for complex spatial relationships. The variable selection is important for the GWR-SAR model to decide whether the SAR term should be included to account for spatial autocorrelation and whether some of its coefficients should be identified as zero for constructing a more appropriate model. Taking these two issues into consideration, we design a data-driven model–the geographically neural network weighted regression with spatial autoregressive (GNNWR-SAR) model. Our model combines the spatial two-stage least squares and neural networks to adaptively capture complex spatial relationships for better estimation, and uses the adaptive Lasso to select variables for model specification. A simulation study demonstrates that our proposed GNNWR-SAR model outperforms the GWR-SAR model in fit, estimation, identifying the pre-designed zero coefficients for the data generated with multicollinearity, and handling spatial autocorrelation. The Boston housing price analysis justifies the applicability of our proposed GNNWR-SAR model.

Credit and House Price Effects of Automated Underwriting Adoption

Uncertain semi-varying coefficient model with application to housing prices

This study proposes a financial model to quantify default risk in jeonse deposits under lease contracts incorporating the right to lease renewal. Recently, such risk has increased structurally due to rising interest rates, housing price fluctuations, and changes in the jeonse-to-price ratio. Jeonse contracts involve a complex interaction of housing prices, deposit levels, volatility, interest rates, and renewal rights, requiring a financial-theoretical framework. Existing studies mainly rely on probability-based models and have limitations in capturing contractual structures and embedded options. In particular, when the right to lease renewal exists, default risk evolves dynamically with contract continuity and market conditions,making single-option frameworks insufficient. To address this, default risk is modeled as a European put option and the renewal right as a European call option, combined into a compound option framework. Results show that default risk increases nonlinearly as housing prices approach deposit levels or as the jeonse ratio rises, with volatility amplifying risk in high-ratio regimes. Interest rates are negatively related to risk, with stronger effects in low-rate environments. The renewal right delays default realization and reduces its present value under certain conditions. This study interprets jeonse default risk as a state-dependent phenomenon driven by contract structure, market conditions, and institutional factors, and provides a quantitative frameworbased on option pricing theory.

ABSTRACT This paper studies the impact of extreme heat on the housing market in 26 Chinese cities by analyzing more than 1.5 million second‐hand housing transaction records. Empirical results from hedonic regressions show that extreme heat significantly decreases housing prices and transaction volumes. This negative effect is concentrated in warm seasons and is substantially stronger for high‐end apartments, top‐floor units, and units in high‐rise buildings. Green spaces and water bodies are effective in mitigating the negative effect of extreme heat on the housing market. Mechanism analysis demonstrates that extreme heat significantly decreases the number of on‐site property visits before a transaction. These findings underscore the important influence of climate change on the housing market.

This study introduces a new two-parameter exponential (TPEX) model for modeling skewed phenomena and risk analysis, motivated by the need for flexible yet tractable models capturing asymmetric behavior in actuarial, financial, and reliability data. An extensive simulation study evaluated seven estimation procedures: maximum likelihood estimation (MLE), ordinary least squares (OrLS), weighted least squares (WLSQ), Cramér–von Mises (CVM), Anderson–Darling estimation (ADE), Kolmogorov estimation (KE), L-moments, and Bayesian estimation, comparing bias, efficiency, and stability across sample sizes and parameter settings. Four real-data applications were conducted: two comparing estimation methods on relief and survival datasets and two assessing competitive performance against exponential-type models. Key risk indicators (KRIs), including the Value at Risk (VaR), Tail Value at Risk (TVaR), Tail Variance (TV), Tail Mean–Variance (TMV), and expected loss (EL), were computed using UK motor non-comprehensive claims and US house price data, illustrating the model’s relevance for insurance reserving and market risk assessment.