The Impact of iBuyers on Housing Market Dynamics

Harrison, David M.; Seiler, Michael J.; Yang, Liuming

doi:10.1007/s11146-023-09954-z

Cited by 5 publications

(1 citation statement)

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“…There are a few studies on the spillover effect on home values from new market participants and literature argues that the entrance of both individual investors (Bayer et al, 2020;Chinco & Mayer, 2016) and institutional investors (D'Lima & Schultz, 2021;Ganduri et al, 2019;Lambie-Hanson et al, 2022), such as buy-to-rent institutions, has a positive effect on home prices. Moreover, the recent presence of iBuyers in the housing market provides liquidity, increases residential mobility (Buchak et al, 2020), and pushes up local housing prices (Harrison et al, 2023). We complement the previous research by analyzing the peer externality effect of disclosing neighbors' gun ownership on property values.…”

Section: Spillovers and Externalities In The Housing Marketmentioning

confidence: 89%

Gun‐ownership disclosure and localized home prices

Seiler,

Yang

2023

Real Estate Economics

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This article examines how the disclosure of local gun‐ownership information affects property values. Using the sudden disclosure of a gun‐ownership map in two New York counties, we explore how home sellers respond to this exogenous information shock. Our results show that an additional permit holder in the neighborhood leads to a 1% decrease in housing prices after the disclosure. This effect is highly localized in that property values are only negatively impacted by gun‐permit holders within 0.1 mi of the focal property. These findings highlight the negative effect of the disclosure of gun‐ownership information on localized home prices.

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Section: Spillovers and Externalities In The Housing Marketmentioning

confidence: 89%

Gun‐ownership disclosure and localized home prices

Seiler,

Yang

2023

Real Estate Economics

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Towards a Better Uncertainty Quantification in Automated Valuation Models

Pollestad,

Næss,

Oust

2024

J Real Estate Finan Econ

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This study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.

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Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin

2024

Journal of Urban Affairs

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The Impact of iBuyers on Housing Market Dynamics

Cited by 5 publications

References 50 publications

Gun‐ownership disclosure and localized home prices

Gun‐ownership disclosure and localized home prices

Towards a Better Uncertainty Quantification in Automated Valuation Models

Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin

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