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House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach

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  • Mahdieh Yazdani

Abstract

In this research we perform hedonic regression model to examine the residential property price determinants in the city of Boulder in the state of Colorado, USA. The urban housing markets are too compounded to be considered as homogeneous markets. The heterogeneity of an urban property market requires creation of market segmentation. To test whether residential properties in the real estate market in the city of Boulder are analyzed and predicted in the disaggregate level or at an aggregate level we stratify the housing market based on both property types and location and estimate separate hedonic price models for each submarket. The results indicate that the implicit values of the property characteristics are not identical across property types and locations in the city of Boulder and market segmentation exists.

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  • Mahdieh Yazdani, 2021. "House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach," Papers 2108.02442, arXiv.org.
    • Handle: RePEc:arx:papers:2108.02442
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    Cited by:

    1. Mahdieh Yazdani, 2021. "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction," Papers 2110.07151, arXiv.org.
    2. Mahdieh Yazdani & Maziar Raissi, 2023. "Real Estate Property Valuation using Self-Supervised Vision Transformers," Papers 2302.00117, arXiv.org.

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