6533b7d6fe1ef96bd126686c

RESEARCH PRODUCT

Multilevel Methodology Approach for the Construction of Real Estate Monthly Index Numbers

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AbstractIn this paper, we evaluate price indices and hedonic price indices for Italian real estate data using multilevel models. The methodology is based on a random coefficient panel data model. We propose a Laspeyres-type price index and hedonic prices indexes based on some characteristics of the sold properties. The multilevel hierarchical analysis has the advantage of allowing the appraisal analysis for groups, and identified in the same sample data the hierarchical structures of market segmentation according to the parameters of the real estate segment. It allows getting a lot of regression functions as the number of groups identified. Obviously, this depends on the sample size and the variability between groups. Specifically, if the data are also grouped by date, the model allows an analysis of the time series which makes possible the calculation of index numbers and the overall monthly index numbers of real estate properties, consistent with collected data.(ProQuest: ... denotes formulae omitted.)In their general meaning, the index numbers are useful indicators to make predictions, to make decisions, and to study price movement trends in the various sectors of the economy. These are frequently used in the analysis of time series and in particular the historical study of long-term trends, seasonal variations, and cyclical developments (Freud and Wilson, 1997). In real estate, the construction of index numbers of real estate prices in different market segments provides important information about real estate trends, investment profitability, and capital appreciation/depreciation (Del Giudice and d'Amato, 2008).For real estate, the index number time series is exhaustive if expressed on a fixed basis. These real estate numbers express the percentage change of a variable over time compared to a fixed period called the base period. The index numbers presented in this paper are direct indices, which are based on real estate market price surveys and are related to specific properties typologies. These monetary indices are developed on annual basis and based on the systematic and continuous collection of market prices (transaction based). The price index number calculation is based on simple price index methods, which determine the unitary market price position indices (average) in the segment between one index and the next. In their application, it is considered that, while the surface characteristics are involved into the calculation of the unit price, the other real estate characteristics are considered under equal conditions, even if they may be different. The property's characteristics can vary over time due to construction or maintenance activities. Therefore, normally the application is based on the use of a type of property in a specific location both defined in such a way as to identify a market segment (Case and Quigley, 1991).The hedonic price index analysis is based on the hedonic price methods that use the real estate market's price as the dependent variable (Bailey, Muth, and Nourse, 1963). To understand the multilevel model, note direct hedonic models consider the real estate characteristics and time characteristics, the latter in the form of dichotomous variables, in correspondence to successive dates; indirect hedonic models are based on regression equations constructed in relation to the various time periods (cross-section) (Diewert, 1976, 2001, 2004). The multilevel model does not consider time variables in the regressions (such as direct models) or build independent equations, one for each time period considered (such as indirect models), but is based on a data hierarchical structure. Typically, the multilevel model is used in presence of data with a hierarchical structure and separates the effects of variables at different levels according to the various groups present in the structure of the data. In practice, it maintains general information (and variability) carried by the entire sample in formulating the functions for each level of analysis (China, 2007). …