Pricing strategies in the Silicon Valley housing market: an update on TOM and recent events

Wan-Hsiu Cheng, Shih-Chieh Chiu, Chia-Yueh Yen and Fu-Chang Yeh

International Journal of Housing Markets and Analysis, 2024, vol. 19, issue 1, 112-130

Abstract: PurposeThis study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market.Design/methodology/approachThe authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity.FindingsThe Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest.Practical implicationsResults reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market.Originality/valueThe experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.

Keywords: Time-on-market; Price differences; Nonlinear hedonic model; Silicon Valley; COVID-19; Fed (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijhmap:ijhma-07-2024-0092

DOI: 10.1108/IJHMA-07-2024-0092

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