A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
XGBoost outperforms other models in predicting housing prices.
GSNE improves house price predictions by embedding geo-spatial context.
This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing mode…
This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metrop…
Satellite imagery improves house price prediction models.
This paper combines and develops the models in Lastrapes (2002) and Mankiw & Weil (1989), which enables us to analyze the effects of interest rate and population growth shocks on housing price in one integrated framework. Based on this model, we carry out policy simulations to examine whether the housing (stock or flow…
Singapore's cooling measures did not increase housing wealth overall.
In recent years, real estate industry has captured government and public attention around the world. The factors influencing the prices of real estate are diversified and complex. However, due to the limitations and one-sidedness of their respective views, they did not provide enough theoretical basis for the fluctuati…
The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\%-3\% when m…
New measure predicts Dutch housing market downturns.
The study models mortgage prepayment risk using stochastic housing market activity.
Spatially weighted conformal prediction improves uncertainty quantification in house price models.
In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to…
I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large…
Study shows house buyers in Christchurch value earthquake risk differently based on time since 2011 quake.
The paper introduces mortgage-rate-adjusted home prices to help buyers and adjust housing indices.
The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sol…
Study shows how algorithmic prediction affects US housing market, reducing racial wealth disparities.
Model explains herding and volatility in urban housing prices.
Study shows houses appreciated more during pandemic due to speculation, not just price uncertainty.
Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such…
In this article, we develop a model for the evolution of real estate prices. A wide range of inputs, including stochastic interest rates and changing demands for the asset, are considered. Maximizing their expected utility, home owners make optimal sale decisions given these changing market conditions. Using these opti…
Machine learning models predict housing prices using macroeconomic factors.
The paper uses graph learning to detect valid instruments in high-dimensional data for house pricing.
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from …
Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT)…
Study finds no consistent return predictability using payout ratios across 16 countries.
We propose a simple probabilistic model to explain the spatial structure of the rent distribution of housing market in city of Sapporo. Here we modify the mathematical model proposed by Gauvin et. al. Especially, we consider the competition between two distances, namely, the distance between house and center, and the d…
Credit expansion led to stronger household leverage cycles during the U.S. business cycle.
We propose a novel multi-layered nonlinear model that is able to capture and predict the housing-demographic dynamics of the real-state market by simulating the transitions of owners among price-based house layers. This model allows us to determine which parameters are most effective to smoothen the severity of a poten…
Speculative bubbles have been occurring periodically in local or global real estate markets and are considered a potential cause of economic crises. In this context, the detection of explosive behaviors in the financial market and the implementation of early warning diagnosis tests are of critical importance. The recen…
Deep Huber QRNs predict Huber quantiles for house prices.
Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture…
Model predicts climate change's impact on real estate prices.
Examines how extending home loan durations affects French households financially.
Unified theory explains housing cycle across metros, showing credit expansion impacts.
This paper uses alternative data to forecast Japanese real estate performance.
Study evaluates and compares traditional and causal machine learning methods for estimating direct price effects of environmental amenities.
The paper refutes standard asset pricing models and introduces new theories.
The paper discusses various practical consequences of treating economics and finance as an inherently dynamic and chaotic system. On the theoretical side this looks at the general applicability of the market-making pricing approach to economics in general. The paper also discuses the consequences of the endogenous crea…
HabitatAgent offers a multi-agent system for transparent housing consultation.
Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a hi…
Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate a house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating existing houses to the Base Flood Elevation (the elevation of the 100-yr flood) plus a freeboard. This reco…
Safe-House secures DeFi by limiting losses and enhancing security.
Double machine learning improves causal effect estimation by relaxing assumptions.
Study shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.