Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).
Parametric specifications for hedonic price equations are estimated using a data set from Alameda and San Francisco Counties and are compared to estimates using a nonparametric technique called locally weighted regression, LWR. LWR permits flexible estimation of the hedonic's curvature at median attributes and is less sensitive than standard regression techniques to the influence of unusual observations. The technique also avoids imposing a single functional form across time and municipalities. The LWR estimates of municipality-specific hedonics are then used to obtain implicit prices for housing attributes and to derive municipality-specific price indices. The results of extensive diagnostic checks of our technique are also reported. Copyright American Real Estate and Urban Economics Association. Abstract Quality differences make estimation of price indexes for real properties difficult, but these can be largely avoided by basing an index on sales prices of the same property at different times. The problem of combining price relatives of repeat sales of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. This method of estimation is more efficient than others for combining price relatives in that it utilizes information about the price index for earlier periods contained in sales prices in later periods. Standard errors of the estimated index numbers can be readily computed using the regression method, and it permits certain effects on the value of real properties to be eliminated from the index.  A statistical model for predicting individual house prices is proposed utilizing only information regarding sale price, time of sale, and location (ZIP code). This model is composed of a xed time eect and a random ZIP (postal) code eect combined with an autoregressive component. The latter piece is applied only to homes sold repeatedly while the former two components are applied to all of the data. In addition, the autoregressive component incorporates heteroscedasticity in the errors. To evaluate the proposed model, single-family home sales for twenty U.S. metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P Case-Shiller model, which is a repeat sales model, and a conventional mixed eects model. It is also shown that the time eect in the proposed model can be converted into a house price index. Finally, the special case of Los Angeles, CA is discussed as an example of history repeating itself in regards to the current housing market meltdown. Abstract Since the seminal work of M. Bailey, R. Muth, and H. Nourse (1963, J. Amer. Statist. Assoc. 58, 933–942), numerous articles have been written about repeat sales and other methods for constructing house price indices. Our justification for producing yet another paper on this subject is to reemphasize fundamentals. We focus on the basic building blocks—asking questions about what the underlying target is, how repeat sales goes about estimating the target, and when a particular index might be used in practice—rather than on more complex, higher level concerns such as statistical or modeling accuracy. We find that much of the debate over index methodology can be distilled to implicit and largely unrecognized disagreement over the desired target or the intended application. Consequently, we contend that paying greater heed to fundamental questions offers significant rewards to both researchers and practitioners.