Repeat-sales Methods Research Articles

«Пузырь» на рынке жилья? Анализ на основе метода повторных продаж для вторичного рынка Москвы и Санкт-Петербурга

A modification of the repeat-sales method is proposed, which allows estimating housing price indices taking into account changes in the qualitative characteristics of the properties being sold over time, as well as limitations in Russian data. The results of evaluation of indices for Moscow and St. Petersburg are given. It is concluded that in real terms and taking into account the change in the quality of purchased objects, the price levels for the secondary housing in these cities at the end of 2021 are comparable to the level of the end of 2012.

Neighborhood Heterogeneity: Implication to the Weighted Repeat Sales Method

ABSTRACT Does the degree of neighborhood heterogeneity, in and of itself, contribute to the cross-sectional price variations beyond that attributable to differences in physical characteristics within the neighborhood? This study presents theoretical and empirical investigations to confirm that the degree of property heterogeneity within the neighborhood can explain a significant portion of the cross-sectional price variations. Using the MLS data in south Florida, we conduct a series of regression analyses that yield consistent and robust results indicating that (1) neighborhood heterogeneity, whether structural or demographic, exhibits significant explanatory power to the pricing variations; (2) the market volatility, in contrast, only explains a small portion of the pricing variation, which confirms the finding of Case and Shiller; and (3) market liquidity, though not a neighborhood heterogeneity measure, does have a significant and negative effect on the pricing variation, suggesting that the availability of comparable sales can reduce pricing variation. These findings can potentially lead to an improvement over the widely-used Weighted Repeat Sales (WRS) method for constructing home price indices.

An evaluation of competing methods for constructing house price indexes: The case of Warsaw

Accurate house price indexes allow governments and planners to make more effective decisions with regard to land use policy. We compare different ways of computing house price indexes for Warsaw over the period 2006–2018 using a detailed micro-level dataset of 101,182 transactions. We find that when a hedonic approach is used, the resulting index is reasonably robust to the choice of method. More problematic is the repeat-sales method, which is widely used in the US. We find that repeat-sales price indexes are unreliable, affected by sample-selection bias, and prone to significant revisions when new periods are added to the dataset. Even for the hedonic approach, the choice of method can become important for smaller datasets. In such cases, the hedonic time dummy and rolling time dummy methods tend to perform better than the hedonic repricing, hedonic imputation and average characteristics methods. In comparison with the index produced by the National Bank of Poland (NBP), we find that in 2006–7 prices did not rise as much, while since the end of 2012 prices have risen faster than indicated by the NBP index. We attribute these differences to a combination of the choice of hedonic method (we use time-dummy while the NBP index uses the repricing method) and different datasets (the NBP dataset is smaller, especially in the earlier years of the sample).

Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach

Conventional wisdom suggests that non-local buyers usually pay a premium for home purchases. While the standard contract theory predicts that non-local buyers may pay such a price premium because of the higher cost of gathering information, behavioral economists argue that the premium is due to buyer anchoring biases in relation to the information. Both theories support such a price premium proposition, but the empirical evidence is mixed. In this study, we revisit this conundrum and put forward a critical test of these two alternative hypotheses using a large-scale housing transaction dataset from Hong Kong. A novel machine-learning algorithm with the latest technique in natural language processing where applicable to multi-languages is developed for identifying non-local Mainland Chinese buyers and sellers. Using the repeat-sales method that avoids omitted variable biases, non-local buyers (sellers) are found to buy (sell) at a higher (lower) price than their local counterparts. Taking advantage of a policy change in transaction tax specific to non-local buyers as a quasi-experiment and utilizing the local buyers as counterfactuals, we found that the non-local price premium switches to a discount after the policy intervention. The result implies that the hypothesis of anchoring biases is dominant.

Urban Zoning for Sustainable Tourism: A Continuum of Accommodation to Enhance City Resilience

While governments around the world are embarking on the path to recovery from the COVID-19 crisis, sustainable tourism planning is crucial, in particular in the hospitality sector, which enhances the resilience of destinations. However, many destination management models overlook the role of urban zoning. Little is known about the impacts of land-use zoning on the hospitality and property industries, especially with the current disruption of short-term peer-to-peer accommodation like Airbnb. Euclidean zoning, also known as effects-based planning, has long been criticised in destination management for its exclusionary nature and lack of flexibility. With exclusionary zoning, property owners may only be able to use their land sub-optimally, and cities will be less efficient in responding to market changes in short-term and long-term accommodation demands, but planning intentions can be better controlled, and the property supply can be more stable. Taking Hong Kong as a noteworthy case, this study puts forward a conceptual framework that enables comparison of a novel zoning approach with the traditional zoning approach. This novel zoning approach encompasses both the short- and long-term rental sectors as a continuum of accommodation, ranging from hotels and serviced apartments to Airbnb and rental housing units under a unified regulatory and planning regime to enhance the switching options value. This novel zoning system can gear up the tourism sector with the rapid growth of the sharing economy and aligns with sustainable tourism to ensure long-term socioeconomic benefits to related stakeholders. We extract the data of Airbnb listings to construct the first Airbnb ADR Index (ADRI) by Repeat-sales method, and the results support our Switching Option Hypothesis.

An Asset Pricing Approach to Liquidity Effects in Corporate Bond Markets

We use an asset pricing approach to compare the effects of the liquidity level and liquidity risk on expected U.S. corporate bond returns. Using signed transaction data, we estimate effective transaction costs for bond portfolios by a repeat-sales method. We find that the liquidity level and exposure to equity market liquidity risk affect expected bond returns. In contrast, exposure to corporate bond liquidity shocks carries an economically negligible risk premium. A simulation study shows that it is unlikely that our results are driven by measurement error in betas or multicollinearity.We present a simple theoretical model that explains these findings.

Cointegration of matched home purchases and rental price indexes — Evidence from Singapore

This paper exploits the homogeneity feature of the Singapore private residential condominium market and constructs matched home purchase price and rental price series using the repeated sales method. These matched series allow us to conduct time series analysis to examine the long-term present value relationship in the housing market. Three key findings are obtained. First, we fail to establish a cointegrating relationship between the home purchase price and rental price based on nationally estimated indexes. Second, area-specific indexes demonstrate strong cross-correlations, invalidating the use of first generation panel unit root tests that ignore these cross-correlations. Third, Pesaran's CIPS test indicates that the unit root hypothesis is rejected for the first difference of both indexes. We also do not reject the hypothesis that area-specific home purchases and rental price indexes are cointegrated with a cointegrating vector (1, −1).

New methodology for constructing real estate price indices applied to the Singapore residential market

This paper develops a new methodology for constructing a real estate price index that utilizes all transaction price information, encompassing both single-sales and repeat-sales. The method is less susceptible to specification error than standard hedonic methods and is not subject to the sample selection bias involved in indexes that rely only on repeat sales. The methodology employs a model design that uses a sale pairing process based on the individual building level, rather than the individual house level as is used in the repeat-sales method. The approach extends ideas from repeat-sales methodology in a way that accommodates much wider datasets. In an empirical analysis of the methodology, we fit the model to the private residential property market in Singapore between Q1 1995 and Q2 2014, covering several periods of major price fluctuation and changes in government macroprudential policy. The index is found to perform much better in out-of-sample prediction exercises than either the S&P/Case-Shiller index or the index based on standard hedonic methods. In a further empirical application, the recursive dating method of Phillips et al. (2015a,b) is used to detect explosive behavior in the Singapore real estate market. Explosive behavior in the new index is found to arise two quarters earlier than in the other indices.

Commercial Real Estate Rental Index: A Dynamic Panel Data Model Estimation

Using the actual quarterly rental income generated in the years between 2001 and 2010 by over 9,000 NCREIF commercial properties, we construct a commercial real estate rental index and estimate the time series properties (e.g., mean‐reversion speed and volatility) of market‐wide rental growth using a dynamic panel data model. The dynamic panel data model has several advantages over a standard hedonic regression. In addition, we incorporate age effects into our panel data model, and by doing so we correct the age bias in the repeated sales method and in the simple average method. Our estimates show that rental growth is cyclical but it generally lags behind broader economic growth. Surprisingly, the long‐term average rental growth is significantly lower than what is usually perceived, and the volatility of rental growth can be significantly under estimated when the conventional methods are adopted. We also find significant cross‐property type and cross‐region variations in the rental adjustment process. In contrast to the existing literature, we find a strong negative relation between rental growth and cap rate, and that this relation is significantly stronger than that between NOI growth and cap rate. Finally, we establish an empirical relation between price return and rental growth in the commercial real estate market.

Daily House Price Indexes: Construction, Modeling, and Longer-Run Predictions

We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit similar characteristics to other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity, which are well described by a relatively simple multivariate GARCH type model. The sample and model-implied correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are all commensurate with existing empirical evidence for the existing monthly and quarterly house price series. A simple model of daily house price index returns produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data, underscoring the informational advantages of our new more finely sampled daily price series.

10.3846/1648715X.2014.973925

This paper investigates tax inequities in assessed values and how these inequities in tax assessments affect house price indices using assessed values statistics. Using the unique rating valuation data from the top 10 cities of New Zealand during the period 1994–2009, it finds that house price measurements using the Sale Price Appraisal Ratio (SPAR) method have performed well compared to the repeated sales method suggested by Case and Shiller (1989) and the assessed values (AV) method proposed by Clapp and Giaccotto (1992). The presence of systematic estimated errors (both vertical and horizontal inequities) in assessed values posts a concern for house price measurements using assessed values statistics. In this situation, both the SPAR and AVmethods benefit from the law of compensation of errors by using all transaction data. A policy implication is that the SPAR model is a good choice when using assessed values to measure house price movements at frequent time intervals, in particular for small countries.

A New Hedonic Regression for Real Estate Prices Applied to the Singapore Residential Market

This paper develops a new hedonic method for constructing a real estate price index that utilizes all transaction price information that encompasses both single-sale and repeat-sale properties. The new method is less prone to specification errors than standard hedonic methods and uses all available data. Like the Case-Shiller repeat-sales method, the new method has the advantage of being computationally efficient. In an empirical analysis of the methodology, we fit the model to all transaction prices for private residential property holdings in Singapore between Q1 1995 and Q2 2014, covering several periods of major price fluctuation and changes in government macroprudential policy. Two new indices are created, one from all transaction prices and one from single-sales prices. The indices are compared with the S&P/Case-Shiller index. The result shows that the new indices slightly outperform the S&P/Case-Shiller index in predicting the price of single-sales homes out-of-sample. However, they underperform the S&P/Case-Shiller index in predicting the price of repeat-sales homes out-of-sample. The empirical findings indicate that specification bias can be more substantial than the sample selection bias when constructing a real estate price index. In a further empirical application, the recursive method of Phillips, Shi and Yu (2014) is used to detect explosive periods in real estate prices of Singapore. The results confirm the existence of an explosive period from Q4 2006 to Q1 2008. No explosive period is found after 2009, suggesting that the ten successive rounds of cooling measures implemented by the Singapore government have been effective in changing price dynamics and preventing a subsequent outbreak of explosive behavior in the Singapore real estate market.

Market Risk Factor and the Weighted Repeat Sales Method

In this paper, we identify a critical issue in the weighted repeat sales (WRS) method—the omission of market risk in the weight estimation model specified by Case and Shiller (1989). We demonstrate that the omission of market risk is conceptually unjustified. We propose a modified WRS model that is empirically supported, but also contributes to the broad discussion on the holding period dependence of real estate risk. We also show that the Case-Shiller weighting method is likely to be mis-specified in nine of the ten cities where the Case- Shiller metro indices are “tradable” with housing options and futures contracts listed on the Chicago Mercantile Exchange. Using a large sample of repeat sales from the Washington DC area, the original repeat sales method of Bailey, Muth, and Nourse (1963) and the Case-Shiller method are compared against the modified WRS. The results indicate that market risk plays an important role in the index estimation.

Cointegration of Matched Home Purchases and Rental Price Indexes -- Evidence from Singapore

This paper exploits the homogeneity feature of the Singapore private residential condominium market and constructs matched home purchase price and rental price series using the repeated sales method. These matched series allow us to conduct time series analysis to examine the long-term present value relationship in the housing market. Three key findings are obtained. First, we fail to establish a cointegrating relationship between the home purchase price and rental price based on nationally estimated indexes. Second, area-specific indexes demonstrate strong cross-correlations, invalidating the use of first generation panel unit root tests that ignore these cross-correlations. Third, Pesaran’s CIPS test indicates that the unit root hypothesis is rejected for the first difference of both indexes. We also do not reject the hypothesis that area-specific home purchases and rental price indexes are cointegrated with a cointegrating vector (1,-1).

Property prices index numbers and derived indices

Purpose – A useful instrument to understand and examine the inner workings of the property trade is devising index numbers of property prices based on historical sequences of market prices. The present work aims at the definition of index numbers of property prices, proposing an innovative methodology compared with what usually recurs in literature. The purpose of this paper is to discuss these issues. Design/methodology/approach – The analysis proposed, based on the mechanisms of formation of stock indices, investigates the analogies between stock and property information, according to the peculiarities of the property trade, leading to a methodology approach, derived from Simple Price Index Method, able to consider possible anomalies in the collected sample of purchase prices, using weighting coefficients based on reliability coefficients of sale prices of properties. Findings – The novel approach proposed has led to the definition of a original methodology useful to appraise property price index numbers and other derived indicators, effective for interpreting and identifying real estate market dynamics in a given area of study, regarded as a standard estimating methodology applicable to any geographical context and kind of property. Practical implications – Methodology proposed in this work is useful to revalue real estate sales price and to consider presence of anomalous sales price in property samples. Originality/value – The calculation of index numbers of prices is usually based on Simple Price Index Methods. Literature shows large use of different methods, such as Repeat Sales Method, Hedonic Price Method, Repeat Value Model. The present work propose an innovative methodology able to detect the presence of possible anomalous market prices in the representative sample, using an appropriate vector of weights in order to take into account the level of reliability of market data.

A Simple Repeat Sales House Price Index: Comparative Properties under Alternative Data Generation Processes

We propose a new method to estimate a repeat-sales house price index. Our unbalanced panel method employs an OLS panel regression to estimate the (log) house price as a function of time fixed effects and house-specific fixed effects. Comparisons are made across three repeat-sales methods using actual data, and using simulated data with both stationary and non-stationary relative price innovations. The unbalanced panel method comprehensively utilises all sale information on a house rather than splitting sales into distinct pairs. It is the simplest of the methods to implement, and possesses superior properties to the other two methods under a wide range of data generation processes.

Managing hedonic housing price indexes: The French experience

Despite their theoretical advantages, hedonic housing price indexes are not so commonly computed by statistical agencies or real estate professionals. Many published indexes still rely on mean or median prices, or favor repeat-sales methods, which require less information about the attributes of the housing units and less econometric expertise on the part of the index compilers, but may be less accurate and robust. In France as in other countries where housing sales have to be recorded in front of a notary, data on transaction prices and characteristics of dwellings are available. Such data have been centralized since 1994, and quarterly hedonic housing price indexes have been computed for more than 10 years. This paper describes the institutional setting of housing transactions in France, and the collaboration between the notaries and the national statistical agency (INSEE). The former are responsible for data collection and regular computation, whereas the latter takes scientific responsibility for the method. The detailed information on the individual properties transacted remains proprietary data, but disaggregated indexes are publicly and freely available. This organization and assignment of roles has proven effective and might be extended to countries with similar institutional settings.

An Empirical Study of Sea View Value by Repeat Sales Method

Sea view is almost always found to have a positive effect, whereas reclamation is believed to exert a negative effect on housing price. However, almost all the previous studies are cross sectional in nature, which cannot disentangle multicollinear factors and spillover effects; and do not directly test the impacts of reclamation works on housing price. This paper aims to test empirically in a longitudinal way the sea view value and the effects of reclamation works on housing price. Aerial photos are exploited to track the stages of the reclamation works. Contrary to the traditional belief, it finds that housing units originally with sea views were still enjoying positive premium in transaction prices after the sea had been reclaimed. It is argued that greenery view is of equal value, which can still be provided on reclaimed land. It has strong practical implication in planning and development because obstruction of sea views is often a claim for property price loss by housing owners.

Developing a House Price Index for The Netherlands: A Practical Application of Weighted Repeat Sales

This paper describes the development of a house price index that has been introduced in May 2005 in The Netherlands. This monthly index, called Woningwaarde Index Kadaster (House Price Index Kadaster), is designed to detect changes in the price of the overall stock of owner-occupied homes. Fifty-five indices are calculated: one overall index, four regional indices, 12 provincial indices and 38 indices based on combinations of region/province and dwelling type. We used Case and Shiller’s geometric Weighted Repeat Sales Model to calculate monthly house price indices. We used recorded data on the sales of over 500,000 owner-occupied homes in The Netherlands, all representing repeat sales between January 1993 and December 2006. The accuracy of the index was determined using the 95% confidence interval. We observed that accuracy might become a problem in smaller sub samples. Revision volatility was explored by comparing the index values computed from all available data until December 2005 with the index values computed from the data available until December 2006. Our analysis showed that revision volatility does not seem to be a major problem to the index. We also explored heteroskedasticity in the Repeat Sales method but did not find conclusive evidence for the proposed heteroskedasticity. Given our target (a geometric mean index value) and the characteristics of the dataset (very large but without property characteristics) the Repeat Sales Method seems to be adequate for calculating a house price index for The Netherlands.

Adjusting for Non-Linear Age Effects in the Repeat Sales Index

A true constant quality real estate price index should measure the general change in price level free from any change in quality over time. In recent years, the repeat-sales method has been widely used to construct constant quality property price indices. Since buildings depreciate over time, a simple repeat-sales index would underestimate the growth in property prices. The major problem of controlling the effects of age constant in a repeat-sales model arises from the exact multicollinearity between the age variable and the time dummy variables. In this study, we derive a solution that is theoretically sound and practical by allowing the age effects to be non-linear. In case of leasehold properties, we further incorporated interest rates into the model because the effects of age on real estate prices depend theoretically on interest rates. A sample of residential units in Hong Kong sold more than once from Quarter 2 of 1991 to Quarter 1 of 2001 (more than 11,000 repeat sales pairs) are used for the empirical analysis.