Reducing revisions in hedonic house price indices by the use of nowcasts

Abstract National Statistical Institutes (NSIs) must balance between timeliness and accuracy of the indicators they publish. Because some of the house sales transactions are reported several months after they occur, many countries that include Israel, publish provisional house price indices (HPIs) that are subject to large revisions as further transactions are reported. This happens because the late-reported transactions behave differently from the transactions reported on time. In this paper, we propose a novel methodology to minimize the size of the revisions, with illustrations from Israel, but the method can be applied to other countries with appropriate modifications. The proposed methodology consists of nowcasting three types of variables at a subdistrict level and adding them as input data to an extended hedonic model used for the computation of the HPI: (1) the average characteristics of the late-reported transactions such as the average number of rooms and the area size of the sold apartments; (2) the average price of the late-reported transactions; and (3) the number of late-reported transactions. The three variables are nowcasted based on models fitted to data from previous months. Evaluation of our methodology shows more than 50% reduction in the magnitude of the revisions.

[1]  Hedonic Price Indexes: A Comparison of Imputation, Time Dummy and ’Re-Pricing’ Methods , 2010 .

[2]  Anthony Garratt,et al.  Real-Time Representations of the Output Gap , 2008, The Review of Economics and Statistics.

[3]  Arthur F. Burns,et al.  Statistical Indicators Of Cyclical Revivals , 1938 .

[4]  N. A. Menzies,et al.  Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States , 2020, medRxiv.

[5]  Jennifer L. Castle,et al.  Nowcasting is not Just Contemporaneous Forecasting , 2009, National Institute Economic Review.

[6]  J. F. Lawless,et al.  Adjustments for reporting delays and the prediction of occurred but not reported events , 1994 .

[7]  R. Shiller,et al.  Prices of Single Family Homes Since 1970: New Indexes for Four Cities , 1987 .

[8]  James Mitchell,et al.  Nowcasting and predicting data revisions using panel survey data , 2009 .

[9]  Danny Ben-Shahar,et al.  Tax Evasion in the Housing Market: Identification and Exploration , 2017, Journal of Real Estate Research.

[10]  Edward S. Knotek,et al.  Nowcasting U.S. Headline and Core Inflation: MONEY, CREDIT AND BANKING , 2017 .

[11]  David H. Small,et al.  Nowcasting: the real time informational content of macroeconomic data releases , 2008 .

[12]  M. Silver,et al.  The Use of Weights in Hedonic Regressions: the Measurement of Quality-Adjusted Price Changes , 2003 .

[13]  Marc K. Francke,et al.  Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models , 2020, The Journal of Real Estate Finance and Economics.

[14]  John M. Quigley,et al.  Program Housing and Urban Policy , 2001 .

[15]  Evan F. Koenig,et al.  VAR Estimation and Forecasting When Data Are Subject to Revision , 2010 .

[16]  Simon van Norden,et al.  Why are initial estimates of productivity growth so unreliable , 2016 .

[17]  Michael P. Clements,et al.  Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets , 2017 .