Online Prediction of Berlin Single-Family House Prices

Rainer Schulz, H. Sofyan, A. Werwatz, R. Witzel

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    Single-family houses are typically the most important component in their owners' portfolios. Buying a home is a major financial transaction for most households. Yet, unlike assets traded in financial markets, getting a quote for the current market value of a house is not an easy task because houses axe very heterogenous assets. They differ, among other things, in size, location, age and maintenance. In this paper, we describe a web-based, almost realtime prediction of prices for single family homes in Berlin, Germany. Based on an extended hedonic regression model and estimated from a rich data set covering all house transactions in Germany's capital, this online service delivers predictions for homes with user-specified characteristics. We describe the statistical model and how its predictions are implemented on the computer to allow seamless interaction between its users and the data base containing the model estimates.

    Original languageEnglish
    Pages (from-to)449-462
    Number of pages13
    JournalComputational Statistics
    Volume18
    Issue number3
    Publication statusPublished - 2003

    Keywords

    • hedonic regression
    • Kalman filtering
    • Java Server Pages
    • REGRESSION
    • ALGORITHM
    • MODELS

    Cite this

    Schulz, R., Sofyan, H., Werwatz, A., & Witzel, R. (2003). Online Prediction of Berlin Single-Family House Prices. Computational Statistics, 18(3), 449-462.

    Online Prediction of Berlin Single-Family House Prices. / Schulz, Rainer; Sofyan, H.; Werwatz, A.; Witzel, R.

    In: Computational Statistics, Vol. 18, No. 3, 2003, p. 449-462.

    Research output: Contribution to journalArticle

    Schulz, R, Sofyan, H, Werwatz, A & Witzel, R 2003, 'Online Prediction of Berlin Single-Family House Prices' Computational Statistics, vol. 18, no. 3, pp. 449-462.
    Schulz, Rainer ; Sofyan, H. ; Werwatz, A. ; Witzel, R. / Online Prediction of Berlin Single-Family House Prices. In: Computational Statistics. 2003 ; Vol. 18, No. 3. pp. 449-462.
    @article{18446cf488a149ddac4f2d4f12858878,
    title = "Online Prediction of Berlin Single-Family House Prices",
    abstract = "Single-family houses are typically the most important component in their owners' portfolios. Buying a home is a major financial transaction for most households. Yet, unlike assets traded in financial markets, getting a quote for the current market value of a house is not an easy task because houses axe very heterogenous assets. They differ, among other things, in size, location, age and maintenance. In this paper, we describe a web-based, almost realtime prediction of prices for single family homes in Berlin, Germany. Based on an extended hedonic regression model and estimated from a rich data set covering all house transactions in Germany's capital, this online service delivers predictions for homes with user-specified characteristics. We describe the statistical model and how its predictions are implemented on the computer to allow seamless interaction between its users and the data base containing the model estimates.",
    keywords = "hedonic regression, Kalman filtering, Java Server Pages, REGRESSION, ALGORITHM, MODELS",
    author = "Rainer Schulz and H. Sofyan and A. Werwatz and R. Witzel",
    year = "2003",
    language = "English",
    volume = "18",
    pages = "449--462",
    journal = "Computational Statistics",
    issn = "0943-4062",
    publisher = "Springer Verlag",
    number = "3",

    }

    TY - JOUR

    T1 - Online Prediction of Berlin Single-Family House Prices

    AU - Schulz, Rainer

    AU - Sofyan, H.

    AU - Werwatz, A.

    AU - Witzel, R.

    PY - 2003

    Y1 - 2003

    N2 - Single-family houses are typically the most important component in their owners' portfolios. Buying a home is a major financial transaction for most households. Yet, unlike assets traded in financial markets, getting a quote for the current market value of a house is not an easy task because houses axe very heterogenous assets. They differ, among other things, in size, location, age and maintenance. In this paper, we describe a web-based, almost realtime prediction of prices for single family homes in Berlin, Germany. Based on an extended hedonic regression model and estimated from a rich data set covering all house transactions in Germany's capital, this online service delivers predictions for homes with user-specified characteristics. We describe the statistical model and how its predictions are implemented on the computer to allow seamless interaction between its users and the data base containing the model estimates.

    AB - Single-family houses are typically the most important component in their owners' portfolios. Buying a home is a major financial transaction for most households. Yet, unlike assets traded in financial markets, getting a quote for the current market value of a house is not an easy task because houses axe very heterogenous assets. They differ, among other things, in size, location, age and maintenance. In this paper, we describe a web-based, almost realtime prediction of prices for single family homes in Berlin, Germany. Based on an extended hedonic regression model and estimated from a rich data set covering all house transactions in Germany's capital, this online service delivers predictions for homes with user-specified characteristics. We describe the statistical model and how its predictions are implemented on the computer to allow seamless interaction between its users and the data base containing the model estimates.

    KW - hedonic regression

    KW - Kalman filtering

    KW - Java Server Pages

    KW - REGRESSION

    KW - ALGORITHM

    KW - MODELS

    M3 - Article

    VL - 18

    SP - 449

    EP - 462

    JO - Computational Statistics

    JF - Computational Statistics

    SN - 0943-4062

    IS - 3

    ER -