ارزش بازار امکانات محیطی: روش متغیر پنهان
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|14328||2000||23 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Housing Economics, Volume 9, Issues 1–2, March 2000, Pages 104–126
This study presents a latent variable framework to provide consistent and efficient estimates of market values of amenities. A model for property values of residential housing using different indicators for neighborhood quality and property value is estimated using data from the U.S. American Housing Survey. The estimated effect of neighborhood quality on property values is positive and more significant compared to the estimates obtained by ordinary least squares and instrumental variable methods. Variances of errors of measurement and variances of the latent structures are shown to be positive and significant without imposing nonnegativity restrictions.
The issue of measurement of environmental improvements has important policy implications, as has been pointed out, for instance, in Bartik and Smith (1987), Baumol and Oates (1988), Smith (1990), and more recently in Cropper and Oates (1992). Adequate measures of the demand for environmental quality provide an indication of the dollar value placed on the benefits of environmental programs. Smith (1990) presents a good description of the problem facing the analyst who needs to measure implicit price values that individuals impute when they consume services provided by environmental amenities.
نتیجه گیری انگلیسی
A latent variable approach to estimate the hedonic price function involving unobservables is suggested. The method consists of an application of the errorin- variable approach when more than one indicator are available. The multiple indicator approach provides a systematic treatment of the errors in measurements in both the regressand and the regressors. Compared to the conventional instrumental variable approach, it not only avoids the arbitrariness of selecting the proxy variables, but it also can capture a richer interaction among the indicators through the estimation of the error covariance matrix.