Home Price Index

A Machine Learning Methodology

Joseph Barr, Eden A. Ellis, Antonio Kassab, Christian L. Redfearn, Narayanan Nani Srinivasan, Kurtis B. Voris

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called `gradient boosted' model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the `median sale' and `repeat sales' indices.
    Original languageEnglish
    Pages (from-to)111-133
    Number of pages23
    JournalInternational Journal of Semantic Computing
    Volume11
    Issue number1
    DOIs
    Publication statusPublished - 2017

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    price index
    Learning systems
    Sales
    methodology
    learning
    sale
    assets
    homeowner
    real estate
    investor
    sales
    Calibration
    geography
    Values

    Cite this

    Barr, J., Ellis, E. A., Kassab, A., Redfearn, C. L., Srinivasan, N. N., & Voris, K. B. (2017). Home Price Index: A Machine Learning Methodology. International Journal of Semantic Computing, 11(1), 111-133. https://doi.org/10.1142/S1793351X17500015
    Barr, Joseph ; Ellis, Eden A. ; Kassab, Antonio ; Redfearn, Christian L. ; Srinivasan, Narayanan Nani ; Voris, Kurtis B. . / Home Price Index : A Machine Learning Methodology. In: International Journal of Semantic Computing. 2017 ; Vol. 11, No. 1. pp. 111-133.
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    abstract = "Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called `gradient boosted' model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the `median sale' and `repeat sales' indices.",
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    Barr, J, Ellis, EA, Kassab, A, Redfearn, CL, Srinivasan, NN & Voris, KB 2017, 'Home Price Index: A Machine Learning Methodology', International Journal of Semantic Computing, vol. 11, no. 1, pp. 111-133. https://doi.org/10.1142/S1793351X17500015

    Home Price Index : A Machine Learning Methodology. / Barr, Joseph; Ellis, Eden A. ; Kassab, Antonio ; Redfearn, Christian L. ; Srinivasan, Narayanan Nani ; Voris, Kurtis B. .

    In: International Journal of Semantic Computing, Vol. 11, No. 1, 2017, p. 111-133.

    Research output: Contribution to journalArticleResearchpeer-review

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    T2 - A Machine Learning Methodology

    AU - Barr, Joseph

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    AU - Kassab, Antonio

    AU - Redfearn, Christian L.

    AU - Srinivasan, Narayanan Nani

    AU - Voris, Kurtis B.

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    N2 - Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called `gradient boosted' model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the `median sale' and `repeat sales' indices.

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    Barr J, Ellis EA, Kassab A, Redfearn CL, Srinivasan NN, Voris KB. Home Price Index: A Machine Learning Methodology. International Journal of Semantic Computing. 2017;11(1):111-133. https://doi.org/10.1142/S1793351X17500015