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Changes in product characteristics on the extensive margin (the addition of new features and the removal of old ones) are an important and hitherto neglected dimension of quality change. Standard techniques for adjusting price indices for new goods cannot handle such changes satisfactorily, and this leads to an economically and statistically significant bias in the measurement of prices and real output. We combine insights from the theories of exact index numbers and demand for characteristics to develop a new method for incorporating changes on the extensive characteristic margin. Applied to U.K. data on new car sales, our method leads to revisions in estimated inflation rates for this commodity group that are both plausible and quantitatively important.

Purpose This study aims to answer the following questions which have not been investigated in the literature to the best knowledge: Is there any bubble in the German housing sector between 2005–2009 and 2012–2017? and Is there any linkage between economic policy uncertainty and the housing sector price index? Design/methodology/approach This study aims to shed some light on the German’s housing sector by investigating the housing sector bubble and the causal link between the housing sector index and economic policy uncertainty in Germany, using GSADF, Granger causality, Toda Yamamoto causality and wavelet coherence tests. Findings The findings reveal that there are some bubbles in the housing sector in Germany for the periods investigated, there is a positive correlation between economic policy uncertainty and housing sector price index at different frequencies and different periods and between 2008 and 2009 and between 2011 and 2013, economic policy uncertainty leads housing sector price index. The consistency of the findings from wavelet coherence is confirmed by the outcomes of Granger causality and Toda Yamamoto causality tests. Originality/value To the best knowledge, this is the first study that empirically investigates the relationship between the housing sector and EPU using a novel wavelet econometric method. In addition, this paper extends the research focused on the associations between the housing sector and EPU, by checking the bubbles in the market in different time horizons by using the longest available data span. Furthermore, the consistency of the findings from wavelet causality is confirmed by the outcomes of Granger causality and Toda Yamamoto causality tests. Finally, compared to the previous literature on the relationship between housing and EPU, the study uses a hedonic index for housing for the first time in the case of Germany.

We study the impact of the 1929 stock market crash on the Paris art market by creating a high-frequency index of artwork sales to measure the arrival of new investors seeking safe-havens. The resul...

Commercial property price indices (CPPIs) are needed to monitor financial stability, guide investment decisions by firms, and improve national accounts. Due to a lack of suitable data, however, reliable CPPIs are often hard to construct. Here we survey the current state of the CPPI literature and assess the contribution of the recently published Eurostat CPPI report.

PurposeCommonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. The purpose of this paper is to overcome these challenges using the authors' approach.Design/methodology/approachThe authors construct a quality-adjusted rent index for the office market in Oslo using detailed data from 14,171 rental contracts.FindingsThe authors show that compositional biases can have a large impact on rental price developments. By adding building-fixed effects to a standard hedonic regression model, the authors show that the explanatory power increases considerably. Furthermore, indices excluding location-specific information, or which include less granular location controls than at the building level, portray quite a different picture of rent developments than indices that do take this into account. The authors also exploit information on contract signature date and find that a more timely detection of turning points can be achieved by using the signature date instead of the more typically used start date of the lease.Research limitations/implicationsThe study is confined to Norwegian data, and an avenue for future research would be to explore if similar results are obtained for other countries. A weakness with the paper is that authors' do not observe quality changes over time, such as renovation. Controlling for time-varying and unit-specific attributes in hedonic models for the commercial real estate (CRE) market would be useful to purge indices further for compositional effects and unobserved heterogeneity. While the authors do control for building-fixed effects, there are additional variations within a building (floor, view, sunlight, etc.) that the authors do not capture. Studies that could control for this would certainly be welcome, both in order to estimate the value of such amenities and to see how it affects estimated rent developments. Another promising avenue for future research is to link data on rental contracts in the CRE market with firm-specific information in order to explore how firm profitability and liquidity may affect rental contracts.Practical implicationsThe authors show that the hedonic index yields a sharper fall in rents after the global financial crisis and more muted developments in the period between 2013 and 2015 than the average rent index. The results show that rents have followed their estimated equilibrium closely and have re-adjusted quickly in periods of deviation. From a financial stability perspective, the risk of a sharp fall in rents is reduced because rents often are in line with their fundamentals.Social implicationsThe authors find that a more timely detection of turning points can be achieved by using information on the signature date. This is an important finding. The financial system is heavily exposed toward CRE, and timely detection of turning points is critical for policymakers.Originality/valueThe financial system is heavily exposed toward the commercial real estate market and timely detection of turning points is of major importance to policymakers. Finally, the authors use our quality-adjusted rent index as the dependent variable in an error correction model. The authors find that employment and stock of offices are important explanatory variables. Moreover, the results show that rents have followed their estimated equilibrium path.

Esta investigación estima el comportamiento del valor de obras de artistas colombianos durante el período 1989-2015 mediante el cálculo de un índice hedónico. Para ello, se utilizan datos de subastas internacionales del portal Artprice.com y la metodología de precios hedónicos; se incorpora, además, el modelo de selección de Heckman con el fin de evitar sesgo de selección. Los resultados muestran cómo la rentabilidad de la inversión en estas obras de arte ha sido inferior al rendimiento de los principales indicadores de renta variable, pero constituye una alternativa de diversificación dentro de un portafolio de inversión.

We analyze long-term art auction sales data focusing on and around financial crisis periods with other investment returns to understand whether art can be considered a safe haven during volatile times or a hedging option in general by analyzing art auction data in a volatile emerging market. Our findings suggest Turkish art returns are either negatively correlated or at low correlation with other investments, including the equity market. We have the view that art can be considered a hedging mechanism on average to enhance returns and to decrease the risk of portfolios and improve diversification. However, we do not discard the safe-haven hypothesis, either. Although the auction data on the crisis period is limited, results of and around crisis periods show art returns are positively correlated with various volatility indices. In addition, the number of art transactions also increases after the crisis years, which may be a sign of liquidity requirement of some investors and an opportunity for buyers. The benefit is visible especially during years of contractions, which do not end with a very severe crisis, since the art auction market liquidity dries if the crisis is severe.

The SNA (System of National Accounts) requires separate estimates for the land and structure components of a commercial property. Using transactions data for the sales of office buildings in Tokyo, a hedonic regression model (the Builder’s Model) was estimated and this model generated an overall property price index as well as subindexes for the land and structure components of the office buildings. The Builder’s Model was also estimated using appraisal data on office building REITs for Tokyo. These hedonic regression models also generated estimates for net depreciation rates which can be compared. Finally, the Japanese Government constructs annual official land prices for commercial properties based on appraised values. The paper compares these official land prices with the land prices generated by the hedonic regression models based on transactions data and on REIT data. The results revealed that commercial property indexes based on appraisal and assessment prices lag behind the indexes based on transaction prices.

PurposeReal estate and stocks are two major asset types in an investor’s portfolio. Therefore, this paper aims to investigate the relationship between these two markets to provide a valuable insight into the process of portfolio optimization and security selection.Design/methodology/approachThis study examines the long-run relationship between residential real estate prices and stock market index in the case of Germany for the period of 2005-2017 by applying time series econometrics techniques. To this aim, this study uses Hedonic House Price Index as a proxy for real estate prices and DAX30 as a proxy for stock prices. Moreover, three additional variables, namely, consumer confidence, credit availability and supply of mortgage loans, are incorporated as control variables to assess the robustness of the results.FindingsObtained empirical results indicate a long-run relationship between stock prices and real estate prices which suggests that in long-run, there is no diversification benefit from allocating stock and real estate assets in a portfolio. This finding is especially important for long-term investors such as pension funds.Originality/valueTo the authors’ best knowledge, this is the first study that empirically investigates the relationship between the real estate market and stock prices using the Hedonic Price Index for the case of Germany.

Since the introduction of the iPhone by Apple in 2007 and Google's Android platform in 2009, the two systems have accounted for a total of 90% of the U.S. smartphone market. Apple, however, reaps m...

Standard house price indices measure average movements of average houses in average locations belonging to an average price segment and hence obscure spatial and cross-sectional variation of price appreciation rates even within a single metropolitan area. This paper combines penalized quantile regression techniques with the hedonic imputation approach to reveal such kind of variation. The method is applied to house transactions from Sydney between 2001 and 2014. The analysis finds significant variation across sub-markets over time and in particular during the boom-and-bust cycle peaking in 2004. Appreciation rates were highest for suburban, low-priced and lowest for inner-city, high-priced houses. This article is protected by copyright. All rights reserved

The focus of the study is to join together regression analysis and index numbers to get quality adjusted price change for rents of office and shop premises. First, we apply simple semilogarithmic linear price models, the fixed effects model (FE), and estimate them by the OLS method. We weight these estimated equations from observations into stratums for unweighted and weighted arithmetic and geometric averages such that the basic algebraic properties of the OLS method are satisfied. We show that the aggregation leads to reparameterization of the FE’s (here stratum effects) of the OLS method for the unweighted and weighted arithmetic and the weighted geometric averages. The solution for the unweighted geometric average is trivial and shown in any statistical textbooks. Second, we apply the Oaxaca decomposition (1973) for the aggregated price models (i.e. stratum aggregates) for these four basic averages and divide the actual price change of these averages into two parts: into quality correction and quality adjusted price changes. For these Oaxaca decompositions of different averages, we apply index number theory to estimate price changes for crude aggregation levels. All our analysis, including the basic and excellent index number formulas (see Vartia and Suopera, 2018), are based on their logarithmic representations. Our focus for the use of the logarithmic representations culminates into the theorem of additive decomposition of the value change by which we can compare the price change estimates based on unit values (i.e. weighted arithmetic averages) and especially Montgomery-Vartia price index. We show explicitly that inadequate control of rented squares leads to serious bias – the actual price change includes not only price changes but also quantity changes, which may dominate causing serious bias for price index numbers. Our test data comes from the KTI Property Information Ltd covering large municipals in Finland. The data do not include certain ID-key for the construction of bilateral price-links and thus a hedonic method is our approach to measure price index for rents of office and shop premises. The weighting into the population level is at the moment impossible because lack of data, we make all inference only for the conditional information set of our data. All statistical and mathematical methods in this study are programmed by the SAS and the user interface for the official production of statistics by the SAS EG.

Measurement of price change is based on averages - also, hedonic price index numbers. As NSIs obtain scannerdata having varying content, question arise: what is correct average to use? We compare in this paper price change estimates based on most known averages and index numbers including quality adjusted methods. All analysis is done in logarithmic scale. The statistical analysis of our data is based on graphical presentations and mathematical analysis is presented mostly in our earlier papers.

Statistics Finland has relatively long experience in constructing indices of prices of old flats using the hedonic approach where regression analysis and classification of flats (i.e. stratification) are combined. Practically this means: First the price model is based on the fixed effect model applied for separate regions and second the quality adjustment is performed within each regional cell in a classification (i.e. inside stratums) using so called Oaxaca decomposition. The hedonic price index is computed by aggregating regional cell level (i.e. stratum level) quality adjusted prices using the logarithmic Laspeyres formula. In this study we develop hedonic price index for new flats. In outline, we use similar methods as in case of old flats, but we develop Oaxaca decomposition in the cell or stratum level for unweighted and weighted arithmetic and geometric means – these are our first new results for hedonic methods. For that we develop new theorems for price aggregation from observed prices into stratum aggregates by logarithmic mean. New theorems of price aggregation are performed to semilogarithmic price models. Our price aggregation form observation level into strata leads to three new decomposition average prices – decompositions for unweighted arithmetic average prices and weighted arithmetic and geometric average prices. The stratum level Oaxaca decomposition for different price aggregates, that is, quality adjusted price changes and quality corrections, are aggregated using several basic (i.e. Laspeyres, Paasche, log-Laspeyres, log-Paasche,…) and excellent index number formulas (i.e. Tornqvist, Fisher, Mongomery-Vartia,…). The price aggregation is performed separately for weighted and unweighted arithmetic and geometric average prices. The construction strategy of index series in this study is based on the base strategy. By this strategy index series are free of chain error (or drift). Our test data is a high-quality register data on all free-market transactions of dwellings in new blocks of flats and terraced houses. Data includes statistical unit specific information of unit prices, quantities, values and of some unit specific quality characteristics form 2010/I to 2018/IV being quarter data. The quarter data includes about 2000 – 3000 observation per a quarter.

Research Highlights: Stumpage price is the most important factor affecting the value of forests. Therefore, an understanding of the factors affecting stumpage prices and trends is critical for effective forest management. Background and Objectives: Chinese fir is the most important fast-growing timber species in China, it is also the tree species with the largest trading volume in the stumpage markets of Southern China. The aim of this study was to analyze the determinants and trends of stumpage prices for Chinese fir timber forests. Materials and Methods: Data on 928 sales of Chinese fir timber forests transacted between 2007 and 2016 were gathered from the stumpage markets in Southern China. We analyzed the relationship between stumpage prices and sales characteristics using the hedonic price method (HPM) and measured the stumpage price index with a dummy time hedonic index. Results: (1) The double logarithmic form of the HPM yielded a more accurate estimate than the semi logarithmic form. The R2ad values in the nine annual prediction models were all above 80%. Stock volume made the greatest contribution to stumpage price, followed by stand age. Stand area had no significant impact on the stumpage price. (2) Stumpage prices of Chinese fir timber forests fluctuated greatly, especially in 2010 and 2015 when the sequential price indexes were 180.01% and 74.95%, respectively. Taking 2007 as the baseline, we calculated the base price index in 2016 to be 197%, with an average annual growth rate of 7.82%. (3) The stumpage market was associated with a higher degree of risk than the timber market. Conclusions: Our findings provide valuable inputs that can guide and facilitate the Chinese government&rsquo, s efforts to optimize resource allocation and standardize the stumpage market.

In this paper, we construct a quality-adjusted rent index for the office market in Oslo. Commonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. Using detailed data from more than 16,000 rental contracts, we show that compositional biases can have a large impact on rental price developments. By adding building fixed effects to a standard hedonic regression model, we show that the explanatory power increases considerably. Furthermore, indices controlling for micro-location portray a different picture of rent developments than indices that do not take this into account. We also document a considerable rent premium for proximity to a metro station. Finally, we exploit information on contract signature date and find that a more timely detection of turning-points can be achieved by using the signature date instead of the more typically used start date of the lease. The financial system is heavily exposed towards the commercial real estate market and timely detection of turning-points is of major importance to policymakers.

In this paper, we construct a quality-adjusted rent index for the office market in Oslo. Commonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. Using detailed data from more than 16,000 rental contracts, we show that compositional biases can have a large impact on rental price developments. By adding building fixed effects to a standard hedonic regression model, we show that the explanatory power increases considerably. Furthermore, indices controlling for micro-location portray a different picture of rent developments than indices that do not take this into account. We also document a considerable rent premium for proximity to a metro station. Finally, we exploit information on contract signature date and find that a more timely detection of turning-points can be achieved by using the signature date instead of the more typically used start date of the lease. The financial system is heavily exposed towards the commercial real estate market and timely detection of turning-points is of major importance to policymakers.

In this paper, we propose a new approach to modelling a real estate price index in Morocco, based on the hedonic approach. The basic idea of ​​this paper is to verify the importance of the characteristics of the real estate in the real estate price. Thus, based on data from the three major cities of the capital region of Morocco (RABAT Region), we estimated a hedonic model that takes into account spatial autocorrelation. The results obtained through this modelling generally confirm that the surface area and location of the real estate (land, house, villa and apartment) have a significant influence on the price of real estate. In addition, and because of the static nature of the database, which refers to one year, we have proposed a new approach to building the real estate price index, namely the stochastic Brownian motion approach. The results claim that this index is in perfect agreement with the real estate price index based on the repeat sales approach used and developed by the Central Bank.

This paper employs a ML-Hedonic approach to quantify the value of uniqueness, a type of "soft" information embedded in real estate advertisements. We first propose an unsupervised learning algorithm to quantify levels of semantic deviation ("uniqueness") in descriptions, the textual portions of real estate advertisements. We then estimated the impact of description uniqueness on real estate transaction outcomes using linear hedonic pricing models. The results indicate textual data disseminate information that numerical data cannot capture, and property descriptions effectively narrow the information gap between structured real estate data and the houses by conveying "soft" information about unique house features. A one standard deviation (0.08) increase in description uniqueness compared to neighboring properties leads to a 5.6% increase in property sale prices and a 2.3-day delay in the closing time, controlling for house characteristics, transaction circumstances, and agent unobservables. This paper provides theoretical and empirical insights on how to utilize the emerging Machine Learning tools in economic research.

Container-as-a-service (CaaS) is a business model that facilitates software developers in organizing, running, managing, and deploying applications using container-based virtualization. The CaaS market is a fast-growing one, leading infrastructure-as-a-service (IaaS) providers into offering CaaS solutions built on top of their IaaS platforms. Given the increasing competition among providers, pricing strategies play a key role in determining the business prospects of CaaS. In this work we propose a methodology to analyze the pricing strategies of the providers, based on a hedonic regression. Following that, we also develop a hedonic price index, based on data collected from popular providers, by identifying a wide set of architectural requirements influencing the pricing strategy. We further analyze the correlations between these requirements and define the subset to be included in the regression analysis, interpreting the results in view of the underlying CaaS market characteristics. The model implementation and the data set are freely available on the web under an open-source license.

Purpose This paper aims to understand housing demand of urban Indian households in terms of housing and household-level characteristics. Because a house is a bundle of certain characteristics which vary across houses, each characteristic has an implicit price. Finding this implicit price for certain important characteristics is the first objective of this study. The second objective of the paper is to compute the income elasticity and price elasticity of housing demand for these cities. Design/methodology/approach To achieve comparable estimates, household-level data from India’s National Sample Survey Organisation housing surveys for the years 2002 and 2008-2009 have been used. A hedonic price function is estimated using ordinary least squares (OLS) and Box-Cox functional forms to estimate the implicit prices of housing characteristics. This exercise is attempted for owned and rented houses separately. Demand function required for computing the elasticities, uses the hedonic price index derived from the implicit prices and household characteristics. Findings The study finds housing demand to be income elastic and price inelastic for the six cities across both the time periods. Originality/value Firstly, this study includes housing characteristics such as individual access to drinking water, modern sanitation facility, separate kitchen, condition of the structure, existence of a road with street light and whether the house is in a slum or non-slum area in the hedonic price function. These variables were not used in any of the earlier studies pertaining to India. Secondly, it uses the Box-Cox non-linear form to derive the hedonic price function, a specification not used earlier. Thirdly, this is the first study analysing housing demand across the six largest Indian cities.

Research background: There are several methods to construct a price index for infrequently traded real estate assets (mainly residential, but also office and land). The main concern to construct a valid and unbiased price index is to address the problem of heterogeneity of real estate or put differently to control for both observable and unobservable quality attributes. The one most frequently used is probably the hedonic regression methodology (classic, but recently also spatial and quantile regression). An alternative approach to control for unobservable differences in assets’ quality is provided by repeat sales methodology, where price changes are tracked based on differences in prices of given asset sold twice (or multiple times) within the study period. The latter approach is applied in renown S&P CoreLogic Case-Shiller house price indices. Purpose of the article: The goal of the paper is to assess the applicability of repeat sales methodology for a major housing market in Poland. Previous studies used the hedonic methodology or mix adjustment techniques, and applied for major metropolitan areas. The most widely known example is the set of quarterly house price indices constructed by NBP — especially for the primary and secondary market. The repeat sales methodology has not been adopted with significant success to date — mainly because of concern regarding relative infrequency of transactions on the housing market in most metropolitan areas (thus a potentially small sample of repeated sales). Methods: The study uses data on repeat sales of residential transactions in Krakow from 2003 to 2015. We apply different specifications of repeat sales index construction and compare respective values to the hedonic price index for Krakow estimated by NBP. Findings & Value added: Findings suggest that repeat sales house sales indices can be used to track price dynamics for major metropolitan areas in Poland. The study suggests problems that need to be addressed in order to get unbiased results — mainly data collection mechanism and estimation procedure.

Property prices are affected by changing market conditions, incomes and preferences of people. Price trends in natural hazard zones may shift significantly and abruptly after a disaster signalling structural systemic changes in property markets. It challenges accurate market assessments of property prices and capital at risk after major disasters. A rigorous prediction of property prices in this case should ideally be done based only on the most recent sales, which are likely to form a rather small dataset. Hedonic analysis has been long used to understand how various factors contribute to the housing price formation. Yet, the robustness of its assessment is undermined when the analysis needs to be performed on relatively small samples. The purpose of this study is to suggest a model that can be widely applicable and quickly calibrated in a changing environment. We systematically study four statistical models: starting from a typical standard hedonic function and gradually changing its functional specification by reducing the hedonic analysis to some basic property characteristics and applying kriging to control for neighbourhood effects. Across different sample sizes we find that the latter performs consistently better in the out-of-sample predictions than other traditional price prediction methods. We present the specific improvements to the traditional spatial hedonic model that enhance the model’s prediction accuracy. The improved model can be used to monitor price changes in risk-prone areas, accounting for changes in flood risk and at the same time controlling for autonomous market responses to flood risk.

Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.

The promotion of energy-efficient appliances is necessary to reduce the energetic and environmental burden of the household sector. However, many studies have reported that a typical consumer underestimates the benefits of energy-saving investment on the purchase of household electric appliances. To analyze this energy-efficiency-gap problem, many scholars have estimated implicit discount rates that consumers use for energy-consuming durables. Although both hedonic and choice models have been used in previous studies, a comparison between the two models has not yet been made. This study uses point-of-sale data about Japanese residential air conditioners and estimates implicit discount rates with both hedonic and choice models. Both models demonstrate that a typical consumer underinvests in energy efficiency. Although choice models generally estimate a lower implicit discount rate than hedonic models, the latter models estimate the values of other product characteristics more consistently than choice models.

Property price indexes are difficult to determine both from the substantive and technical/organizational points of view. Various methods of constructing such indexes have been developed in order to overcome these difficulties. To this end, the author compares two types of indexes: hedonic indexes and ones termed filtered for the purpose of this particular paper. Hedonic index values come from Polish National Bank (NBP) publications, while the filtered indexes have been computed with the use of the 4253H filter on the basis of the NBP announcements on mean property prices. Thus, the results are comparable as both types of indexes are derived from the same input databases. The analysis covers both the comparison of the obtained results as well as a discussion of substantive and technical problems encountered when building the property price indexes.

House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

One of the main approaches to constructing quality‐adjusted price indexes is the time dummy hedonic method. An alternative but rather unconventional method is the estimation of quality‐adjusted unit value indexes. An advantage of the latter method is the interpretation of the implicit quantity index as the simple ratio of quality‐adjusted or standardized quantities. In this paper we compare the two methods. We show that the expenditure‐share weighted time dummy price index and the quality‐adjusted unit value index can be written as ratios of weighted geometric and harmonic means, respectively, of quality‐adjusted prices. Next, we argue that the two indexes will have similar trends and volatility if the quality‐adjusted prices in the quality‐adjusted unit value index are based on the estimated time dummy model. Our theoretical findings are illustrated on New Zealand scanner data for seven consumer electronics products.

Although geometric hedonic price indexes are widely used in the hedonic literature on non-standard investments, it is well known that portfolios of assets have values that are related to the arithmetic, not geometric, average of prices. This paper explores the implications of the use of arithmetic hedonic price indexes for art investments. Log-linear predictions are retransformed back to the original scale by performing a modified version of the Duan’s smearing factor. This procedure is illustrated with an analysis of the returns from 10,459 Surrealist paintings sold worldwide during the pre-crisis period 1990–2007. Findings show significant differences between the arithmetic versus geometric price indexes, while emphasising the caution that should be exercised in interpreting the hedonic index prices of non-standard investments as they are typically computed for academic and business applications.

Determining how and when to use geospatial data (i.e. longitudes and latitudes for each house) is probably the most pressing open question in the house price index literature. This issue is particularly timely for national statistical institutes (NSIs) in the European Union, which are now required by Eurostat to produce official house price indexes. Our solution combines the hedonic imputation method with a flexible hedonic model that captures geospatial data using a non-parametric spline surface. For Sydney, Australia, we find that the extra precision provided by geospatial data as compared with postcode dummies has only a marginal impact on the resulting hedonic price index. This is good news for resource-stretched NSIs. At least for Sydney, postcodes seem to be sufficient to control for locational effects in a hedonic house price index.

As an immobile, durable and heterogonous good, each housing unit has a bundle of different characteristics. Hedonic price method, which depends on the consumer theory of the classical economics, implies that each characteristic of heterogeneous goods provides a different level of satisfaction or utility to the consumer, being widely accepted as a toolkit for estimating effects of these characteristics on prices and rents. HPM expresses housing prices as the function of structural characteristics, location characteristics, and neighborhood characteristics. Theory and empirical applications of the HPM, which have been used for more than 40 years in developed countries, due to the lack of suitable data related literature is limited in Albania. The aim of this paper is to analyze the relationship between housing rents and housing characteristics in urban Albania with micro database of Living Conditions and Income Survey 2012.

Many markets exhibit price dispersion across suppliers of observationally identical goods. Statistical agencies typically assume this dispersion reflects unobserved quality, so standard price indexes do not incorporate price declines when buyers substitute toward lower-price suppliers. We show that long-run price differences across suppliers can be used to infer unobserved quality differences and propose an index that accommodates quality-adjusted price dispersion. Using transaction-level data on contract semiconductor manufacturing, we document substantial quality-adjusted price dispersion and confirm that a standard index is biased above our proposed index.

This article uses a hedonic modelling approach to assess the implicit willingness to pay for the visual accessibility of voluntarily protected, privately owned, scenic lands based on single family houses. These lands are perpetually protected to preserve natural, historic, and scenic characteristics. The capitalized house premium was captured using a visual accessibility variable, which was a combined weighted measure of ‘view’ and ‘proximity,’ referred to here as the Gravity Inspired Visibility Index. Both global ( adjusted R2 = 0.52, AICc = 29,828) and geographically weighted regression models ( adjusted R2 = 0.59, AICc = 29,729) estimated the price effect but the geographically weighted regression model outperformed the global model. The results from the geographically weighted regression model indicated an average 3.4% price premium on the mean value of homes in the study area. The paper offers a useful framework for evaluating the effect of land protection for planning and real estate purposes. It also offers useful insights for conservation agencies, local governments, professional planners, and real estate professionals for prioritizing land sites with scenic views.

The Producer Price Index (PPI) for the United States suggests that semiconductor prices have barely been falling in recent years, a dramatic contrast to the rapid declines reported from the mid-1980s to the early 2000s. This slowdown in the rate of decline is puzzling in light of evidence that the performance of microprocessor units (MPUs) has continued to improve at a rapid pace. Over the course of the 2000s, the MPU prices posted by Intel, the dominant producer of MPUs, became much stickier over the chips' life cycle. As a result of this change, we argue that the matched-model methodology used in the PPI for MPUs likely started to be biased after the early 2000s and that hedonic indexes can provide a more accurate measure of price change since then. MPU prices fell rapidly through 2004 on every price measure we present, with the PPI declining at an even quicker pace than the hedonic indexes. However, from 2004 to 2009, our preferred hedonic index fell faster than the PPI, and from 2009 to 2013 the gap widened further, with our preferred index falling at an average annual rate of 42 percent, while the PPI declined at only a 6 percent rate. Given that MPUs currently represent about half of U.S. shipments of semiconductors, this difference has important implications for gauging the rate of innovation in the semiconductor sector.

Estimating house prices is essential for homeowners and investors alike with both needing to understand the value of their asset, and to understand real estate assets as part of an overall portfolios. Commonly-used indices like the National Association of Realtors (NAR) median home price index, or the celebrated Case-Shiller Home Price Index are reported exclusively over a large geographic areas, i.e., a metropolitan, whereby home price dynamics are lost. In this paper, we propose a improved method to capture price dynamics over time at the most granular level possible — a single home. Using over 16 years of home sale data, from the year 2000 to 2016, we estimate home price index for each house. Once home price dynamics is captured, its possible to aggregate price dynamics to construct a price index over geographies of any kind, e.g., ZIP code. This particular index relies on a so-called ‘gradient boosted’ model, a methodology framework relying on multiple calibration parameters and heavily dependent on sampling techniques. We demonstrate that this approach offers several strengths compared to the commonly reported indices, the ‘median sale’ and ‘repeat sales’ indices.

PurposeThis study aims to show how hedonic methods can be used to compare the performance of the public and private sector housing markets in Costa Rica.Design/methodology/approachHedonic price indexes are computed using the adjacent-period method. Average housing quality is measured by comparing hedonic and median price indexes. The relative performance of the public and private sector residential construction is compared by estimating separate hedonic models for each sector. A private sector price is then imputed for each house built in the public sector, and a public sector price is imputed for each house built in the private sector.FindingsThe real quality-adjusted price of private housing rose by 12 per cent between 2000 and 2013, whereas the price of private housing rose by 9 per cent. The average quality of private housing rose by 45 per cent, whereas that of public housing fell by 18 per cent. Nevertheless, the hedonic imputation analysis reveals that public housing could not be produced more cheaply in the private sector.Social implicationsThe quality of public housing has declined over time. The hedonic analysis shows that the decline is not because of a lack of competition between construction firms in the public sector. An alternative demand side explanation is provided.Originality/valueThis study applies hedonic methods in novel ways to compare the relative performance of the public and private housing sectors in Costa Rica. The results shed new light on the effectiveness of public sector housing programs.

Estimated coefficients in hedonic price models are generally assumed to be constant throughout the entire study area. However, increasing evidence reveals that the marginal prices of housing characteristics may vary over space and that the spatial heterogeneity problem in implicit housing prices should be given attention. Taking Hangzhou, China, as an example, this study uses the micro data of 603 residential communities in 2014 to examine spatial heterogeneity in implicit housing prices. On the basis of the traditional hedonic price model, we establish spatial expansion and geographically weighted regression (GWR) models for comparative analysis. Results show that the spatial expansion and GWR models have excellent goodness of fit and can improve the traditional hedonic price model. The mixed geographically weighted regression (MGWR) model further reveals that the implicit prices of nine housing characteristics vary significantly over space and that the impacts of the four remaining housing characteristics on housing prices are fixed throughout the entire study area. Unlike the traditional hedonic price model and spatial expansion model, the GWR/MGWR model has the unique advantage of visually providing the spatial distribution of implicit housing prices and accurately describing spatial heterogeneity.

This study is based on scanner data from large retailers sent daily to Insee in 2013. Its aim is to calculate indices that measure differences in consumer price levels between different areas of metropolitan France, focusing specifically on food products sold in supermarkets. A hedonic index based on the regression of the product price on barcode and territory dummies is developed. Several assessments are carried out over different weeks, with one week of data already providing a great degree of accuracy. The dispersion of price levels between regions or large conurbations is limited and, for the most part, robust to the choice of week. The highest prices are found in the Paris region and Corsica, with a magnitude of differences in the order of a few percentage points. A comparison of the new findings with research conducted by Insee between 1970 and 2000 shows that differences in food prices across different areas of metropolitan France are essentially structural and change little over time., Léonard Isabelle, Sillard Patrick, Varlet Gaëtan, Zoyem Jean-Paul. Spatial Differences in Price Levels between French Regions and Cities with Scanner Data. In: Economie et Statistique / Economics and Statistics, n°509, 2019. Big Data and Statistics. Part 2. Big Data in the Consumer Price Index. pp. 69-82.

Using a newly available panel data set containing property-specific, time-varying hedonic characteristics and sales prices, we develop a new dynamic house-price model that is suitable for out-of-sample forecasting applications such as mortgage valuation and bank stress-testing. The model is set up in a classical state-space framework and includes common factors that are univariate structural time series models scaled to form linear combinations specific to locations. Our common factors include trend, seasonal, and autoregressive components. The equations are linear and errors are Gaussian; however, the unbalanced nature of our panel data means that standard Kalman-filter smoothing algorithms are not suitable. Instead, we apply an alternative three-block Markov Chain Monte Carlo algorithm Strickland, Turner, Denham, and Mengersen (2009). We find significant in- and out-of-sample forecasting differences between our model and standard repeat-sales and hedonic regressions. We also test, and reject, two assumptions of repeat-sales estimators: a single common trend for all regions and a unit root in the index.

During the decades prior to the Civil War, Spain experienced a rapid process of urbanization, which was accompanied by the demographic transition and sizeable rural–urban migrations. This article investigates how urban housing markets reacted to these far-reaching changes, which increased demand for dwellings. To this end, this study employs a new hedonic index of real housing prices and constructs a cross-regional panel dataset of rents and housing price fundamentals. This new evidence indicates that rents were not a significant financial burden on low-income families and, hence, housing was affordable for the working classes. The article also shows that families’ access to new homes was facilitated by a sizeable growth in the housing supply. Substantial investments in urban infrastructure and the institutional framework enabled the construction of new homes at affordable prices. Our results suggest that housing problems were not as pervasive during the urban transition as the literature often seems to claim.