Non-parametric hedonic housing prices

被引:43
|
作者
Mason, C [1 ]
Quigley, JM [1 ]
机构
[1] UNIV CALIF BERKELEY,BERKELEY,CA 94720
关键词
D O I
10.1080/02673039608720863
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The hedonic price function has long been a standard tool for modeling the price of complex commodities, such as housing. The theoretical basis of the model is sound and appealing, but applications often encounter difficulties. The results of hedonic models depend on inclusion of the right independent variables and the correct specification of the functional form. The functional form assumption is particularly difficult in the housing context because the hedonic price function summarizes not only consumer preferences and production technology, but also various quantities which are historically determined, difficult to measure, and not approachable by theory. In this paper, the functional form assumption is relaxed by estimating the hedonic price function as a General Additive Model (GAM). The GAM is considerably move general than conventional hedonic models and offers significant advantages in comprehensibility over other non-parametric procedures. The model is used to analyze the substantial decline in condominium house values in downtown Los Angeles during the 1980s-a period in which apartments lost about 40 per cent of their values.
引用
收藏
页码:373 / 385
页数:13
相关论文
共 50 条
  • [41] HEDONIC PRICES AND EQUILIBRIUM SORTING IN HOUSING MARKETS: A CLASSROOM SIMULATION
    Anderson, Soren T.
    Bates, Michael D.
    NATIONAL TAX JOURNAL, 2017, 70 (01) : 171 - 183
  • [42] Nonparametric neural network modeling of hedonic prices in the housing market
    Manuel Landajo
    Celia Bilbao
    Amelia Bilbao
    Empirical Economics, 2012, 42 : 987 - 1009
  • [43] The fusion of parametric and non-parametric hypothesis tests
    Singer, PF
    FUSION 2003: PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE OF INFORMATION FUSION, VOLS 1 AND 2, 2003, : 780 - 784
  • [44] Parametric and non-parametric methods for linear extraction
    Bascle, B
    Gao, X
    Ramesh, V
    STATISTICAL METHODS IN VIDEO PROCESSING, 2004, 3247 : 175 - 186
  • [45] Parametric and non-parametric unsupervised cluster analysis
    Roberts, SJ
    PATTERN RECOGNITION, 1997, 30 (02) : 261 - 272
  • [46] PARAMETRIC HYPOTHESES TESTING WITH NON-PARAMETRIC TESTS
    TYURIN, YN
    THEORY OF PROBILITY AND ITS APPLICATIONS,USSR, 1970, 15 (04): : 722 - &
  • [47] A note on combining parametric and non-parametric regression
    Rahman, M
    Gokhale, DV
    Ullah, A
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 1997, 26 (02) : 519 - 529
  • [48] Non-parametric dependent components
    Klami, A
    Kaski, S
    2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING, 2005, : 209 - 212
  • [49] Multiinput non-parametric detector
    Polykov, V.A.
    Tolparev, R.G.
    Radiotekhnika, 1991, (12): : 30 - 32
  • [50] A non-parametric coverage interval
    Lin, Shuo-Huei
    Chan, Wenyaw
    Chen, Lin-An
    METROLOGIA, 2008, 45 (01) : L1 - L4