Construction Of Price Index Research Articles

An approach for constructing spatially paired pseudo repeat-sales housing price indices in China

AbstractConstructing a reliable housing price index is crucial for accurately reflecting housing price dynamics and enhancing transparency in the housing market. However, existing methods for constructing housing price indices often confront many challenges, such as omitted variable issues in the hedonic model and constrained samples used in the repeat-sales model. To address these challenges, this study identifies a spatially paired relationship between complexes and develops a Spatially Paired Pseudo Repeat-Sales model to construct the housing price index. This approach offers two significant advantages: first, it enlarges the sample size used in the repeat-sales model at least 1.8 times; second, it effectively estimates the effects of spatial dependency and physical housing factors on prices while mitigating the impacts of unobservable factors through differentiation. The findings of this paper suggest that using the spatially paired pseudo repeat-sales model can significantly improve the estimation of housing price volatility, by approximately 13%. Moreover, the constructed housing price index model demonstrates significant robustness, even when the spatial weight settings are altered. This research provides convincing evidence of improved housing market transparency for stakeholders, including governments, institutional developers, and individual investors. Additionally, the constructed model can inform other similar research in housing price index construction by providing a spatially pairing perspective on housing complexes.

Price index for computer games: What is hidden from the naked eye?

This paper presents a study of the price dynamics of a wide range of video games in the time period from 2008 to 2020. During the research, a necessary database for analysis was created, video game prices were modeled using econometric methods, and price indices were constructed for various game genres: RPG (Role-Playing Game), strategy (Strategy), action (Action), adventure games (Adventure), causal games (Casual), indie games (Indie), simulators (Simulators) etc. Hypothetically, the price range of video games by genre and system requirements were due to the different amount of effort required to create them. During the construction of hedonistic price indices, prices were cleared from the characteristics of the quality of products (computer games). Video game system parameters were used as characteristics. The work also uses various approaches to modify hedonistic indices by considering genre heterogeneity. Thanks to the study, it is possible to track the dynamics of the behavior of the cost of video games over the considered period of time, with the representation of lagging and advancing trends. Genre diversification of the price elasticity of key variables used during the construction of models was provided.

Constructing Limited-Revisable and Stable CPPIs for Small Domains

Constructing price indices for commercial real estate (CPPIs) is challenging due to heterogeneous and limited observations. Common price index methods often result in volatile index series. Attempts to reduce volatility often lead to frequent revisions of the entire index series and a loss of methodological index properties. When it comes to CPPIs in official statistics, both volatility and frequent revisions are undesirable. Revisions could compromise the confidence of users if indicators are allowed to change indefinitely, while instable indices insufficiently reflect structural underlying developments. In this study, a combination of hedonic imputation, multilateral calculations, time series analysis, and window splicing is introduced. The result is a method that produces stable and limited-revisable indices with the ability to detect turning points in an early stage. Commercial real estate transactions in the Netherlands are used to empirically test the method. The resulting CPPIs appear suitable for monitoring financial stability and, therefore, seem appropriate for the use in official statistics.

PRICE RECALCULATION MODEL OF CONSTRUCTION CONTRACTS

In recent years, the world economy has experienced rapid economic change in the construction sector and record inflation. The rapidly rising prices of construction materials and machinery in the construction market are also pushing up the cost of construction services. The main problem is that construction projects have been drawn up and contracts between clients and contractors have been awarded at previous prices, and it is therefore necessary to look for opportunities to index the price of construction contracts. The aim of the study is to propose a mathematical model and a smartphone application for the price recalculation of construction projects. The success of a construction project depends on the correct decisions taken at the stage of preparation and implementation of the procurement documents for construction contracts, a complex process requiring a lot of legal and technological knowledge. The proposed mathematical model would help clients and contractors to carry out the calculation of the price according to the construction price index quickly and without the need for extensive specialist technical expertise. The mathematical model is adapted to smartphones with Android software.

Construction Price Index Prediction through ARMA with Inflation Effect: Case of Thailand Construction Industry

Over-budgeting due to inflation is a common phenomenon in the construction industry of both developed and developing countries. Inflation, with time changes, leaves an adverse effect on the project budget. Hence, this study aims to focus on the construction price index (CPI) behavior and inspect its correlation with inflation in Thailand’s construction industry as there has not been much work performed. The prediction of CPI was made from 2024 to 2028, relying on the data set from 2000 to 2023. The relationship between inflation and CPI categories helps in prediction by considering inflation as the independent variable and CPI (All Commodities, Lumber and Wood Products, Cement, and Iron Products) as the dependent variable that was incorporated in EViews to perform automated ARIMA forecasting. The correlation results show that out of four CPI, only Iron Products showed a significant relationship with inflation. For All Commodities, Lumber, and Wood Products, the predicted values were fluctuating, while for Cement and Iron Products, a clear seasonal pattern was observed. This prediction gives a direction to construction industry practitioners to make necessary adjustments to their budget estimation before signing the contract to overcome cost overrun obstruction.

A flexible method of housing price index construction using repeat‐sales aggregates

AbstractThe major issue which we address in this article is the one‐size‐fits‐all nature of the typical city‐level housing price index. In this vein, we make two contributions. First, we develop a new algorithm to ensure feasible estimation of geographically granular repeat‐sales price indices in cases of low transactions counts. This facilitates the estimation of a balanced panel of 63,084 U.S. Census tract‐level indices (2010 definitions) at an annual frequency between 1989 and 2021, which we release alongside this article. Second, we use these indices to estimate city‐level price indices that are robust to heterogeneous submarket appreciation and nonrandom sampling, two issues that confound classic approaches. Different index targets require alternative weighting schemes, and these formulations can result in index differences that can widen over time horizons. However, in some cases, sample‐based indices are quite similar to more strictly defined index targets; for instance, in the early COVID‐19 period, standard sample‐based indices are actually quite similar to a unit‐representative house price index for large cities.

Construction of a Comprehensive Energy Price Index for Prefecture-level Cities: The Case of Shanxi Province

Construction of a Comprehensive Energy Price Index for Prefecture-level Cities: The Case of Shanxi Province

The Computer and the Statistician. Computerization and the Mobilization of Scanner Data by National Statistical Institutes for the Construction of Price Indexes

The Computer and the Statistician. Computerization and the Mobilization of Scanner Data by National Statistical Institutes for the Construction of Price Indexes

Determinants of Profitability : Evidence from Construction Companies Listed on Indonesian Securities Market

An asset management role is required to analyze and optimize revenue sources and efficient systems to reduce costs. Construction service companies that tend to have fewer fixed assets than their current assets have constraints in suppressing production prices and must be able to manage them efficiently to maintain their existence in the market. The data shows the growth in the number of construction service companies which continues to increase and the construction price index which continues to decline, requiring efficient asset management to reduce production costs. For this reason, this study aims to determine the effect of Account Receivable Turnover (ARTO) asset management instruments, Fixed Asset Turnover (FATO), Inventory Turnover (IT), and Total Asset Turnover (TATO) on Operating Profit Margin (OPM) in construction service companies in Indonesia. The research sample is 5 large-scale construction companies in Indonesia with vulnerable observation periods from 2017 to 2021. The method used in this research is panel data regression to examine the relationship between research variables. The results of this study indicate that Fixed Asset Turnover (FATO) and Total Assets Turnover (TATO) have an effect on the Operational Profit Margin (OPM) of construction service companies in Indonesia. Meanwhile, Account Receivable Turnover (ARTO) and Inventory Turnover (IT) have no effect on OPM in Indonesian construction service companies. The results of this study have implications for construction service companies that asset management is very important for the company's progress, especially in increasing profits. This research contributes to the ability of construction service companies to manage their assets effectively, because the construction service business actor is the party responsible for implementing infrastructure development in Indonesia.

House price indexes: an empirical exercise using spatial data analysis.

The housing market occupies a central place within any society. Residential property represents the essential parts of most families’ wealth, as well as a substantial part of their monthly expenditure. It also represents a large fraction of private investment, generating significant earnings. However, and despite this importance, the compilation of reliable residential property price indexes (RPPI) is far from satisfactory in Portugal, revealing an important gap in the field of statistical information. The construction of a residential property price index (RPPI) poses particular problems arising from the inherent nature of the object concerned. Considering the gap in Portugal regarding the lack of a sufficiently enlarged database allowing the compilation of reliable RPPIs, this article seeks to contribute to this area, proposing a methodology applied to the urban area of Aveiro and Ílhavo by means of a spatial data analysis.

Examining key macroeconomic determinants of serviced apartments price index: the case of Kuala Lumpur, Malaysia

PurposeIncreasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.Design/methodology/approachTo achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).FindingsResults of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R2 of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.Practical implicationsTherefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.Originality/valueBy adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

Analysis of sun-and-beach markets on large islands in the mediterranean using tourist acommodation prices as an indicators Edit Download

Purpose – This paper uses the prices of holiday accommodations on large Mediterranean islands specialising in sun and beach tourism to determine their initial state in 2015, their competitiveness and their further evolution until 2019. Methodology/Design/Approach – Following the process of each potential tourist, a total of 21,628 prices were collected from the Internet. Then, the collected data were used in the construction of charts and price indices and the application of a method of cluster analysis (HAC). Findings – Between 2015 and 2019, tourism prices in the Mediterranean region increased sharply. Moreover, the geographic segmentation already observed in 2015 between Western and Eastern Mediterranean destinations intensified. In addition, the paper shows that there is robust demand for sun and beach tourism in island destinations. Originality of the research – Major island markets specialising in sun and beach tourism were used to exclude any interference from professional travellers, people visiting friends or relatives, or tourists willing to bear the cost of travel to less visited destinations.

Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network

Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) based on the historical CPI data. The results show that performance of both the models is good in predicting CPI, but performance of the SVM model (R2 train = 0.915, R2 test = 0.811) is the best in comparison to RBFN model (R2 train = 0.985, R2 test = 0.733). The proposed AI models can be used to quickly and accurately predict the CPI of an area to help management agencies, investors, construction contractors for assessing cost of construction for the purchase and development of properties/ infrastructures.

Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network

Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) based on the historical CPI data. The results show that performance of both the models is good in predicting CPI, but performance of the SVM model (R2 train = 0.915, R2 test = 0.811) is the best in comparison to RBFN model (R2 train = 0.985, R2 test = 0.733). The proposed AI models can be used to quickly and accurately predict the CPI of an area to help management agencies, investors, construction contractors for assessing cost of construction for the purchase and development of properties/ infrastructures.

Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network

Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) based on the historical CPI data. The results show that performance of both the models is good in predicting CPI, but performance of the SVM model (R2 train = 0.915, R2 test = 0.811) is the best in comparison to RBFN model (R2 train = 0.985, R2 test = 0.733). The proposed AI models can be used to quickly and accurately predict the CPI of an area to help management agencies, investors, construction contractors for assessing cost of construction for the purchase and development of properties/ infrastructures.

Dynamics of apartment building renovation investment costs based on Estonian reovation grant scheme

While energy prices have increased substantially recently, we also observe that renovation prices have increased twofold wihtin last decade. This could seriously affect the motivation to invest in building renovations because of negative yield and in turn jeopardise the carbon reduction plans. Therefore, we attempt to show some evidence of renovation cost dynamics based on sample of 112 apartment buildings which received state support for deep energy renovations during 2010 and 2017. We found that investments started out very cautiously in 2010. Eventually renovations were embraced as rational investment and renovations ambitions grow with prices. Construction price index grow during the~study period 20 percent and renovations over two times. We could not confirm the popular hypothesis that demand pressure will also increase prices. However, most significant increase of renovation price was due to the additional construction works and quality improvement. This was especially evident after the redesign of grant rules in 2015 which allowed more indirect construction works to be eligible for the subsidy.

Do emerging art market segments have their own price dynamics? Evidence from the Chinese art market

This study examines the nonlinear dynamics in the price series of Chinese art market segments between 2000 and 2019. We employ a hedonic price model to construct price indices of Chinese art market segments and analyze the nonlinearities and regime-switching properties of the individual segment using a series of Markov switching model specifications. We argue that occasional shocks would only temporarily alter their data-generating processes and have transitory effects. Moreover, we investigate the impact of COVID-19 on Chinese art market segments. Our findings have implications for market participants in identifying the price characteristics and dynamic behavior of art market segments.

Probability Distribution Modeling of Scanner Prices and Relative Prices

The article deals with the problem of the proper selection of the theoretical distribution to describe the empirical distribution of scanner prices. In the empirical study we use scanner data from one retail chain in Poland, i.e. monthly data on natural yoghurt, drinking yoghurt, long grain rice and coffee powder sold in 212 outlets in January and February 2022. Prices and price relatives were modeled using selected ten probability distributions with non-negative support, including two, three and four-parameter family of distributions In addition to the visual assessment in the form of empirical PDF and CDF figures, numerical criteria were used. These include information criteria values such as AIC, BIC, HQIC and p values calculated for the K-S, AD and CVM goodness-of-fit tests. Our research showed that at least two models could be distinguished as very accurate, which provides a good background for simulation research on price indices or for the construction of so-called population price indices.

ИНДЕКСНЫЙ АНАЛИЗ ЦЕНОВЫХ ИЗМЕНЕНИЙ НА РОССИЙСКОМ РЫНКЕ ГИНЕКОЛОГИЧЕСКИХ ЛЕКАРСТВЕННЫХ ПРЕПАРАТОВ

Abstract. The article presents the results of the analysis of the level and dynamics of prices in the Russian market of gynecological preparations. Gynecological diseases are a serious obstacle in maintaining a high level of women's reproductive health. In accordance with official statistics, the number of gynecological patients is increasing every year in Russia, and there is also a “rejuvenation” of inflammatory and malignant diseases. In such conditions, the question of the quality and availability of drug provision for women with gynecological preparations arises, which, in turn, depends on the level and dynamics of prices in the corresponding market segment. An analysis of the dynamics of price changes in the Russian market of gynecological preparations was carried out using the index method of analysis, based on the construction and calculation of price indices and allowing to identify factors that affect price growth. It has been established that prices on the Russian market of gynecological drugs have been growing for a long period of time at a rate comparable to the growth rates of prices on the entire Russian pharmaceutical market, however, in certain time periods, the discrepancies can be significant and exceed 5% per year. As a result of the study, groups of gynecological drugs with the highest price growth rates were identified, among which drugs not included in the Vital and Essential Drugs List, domestic and generic drugs can be distinguished. Among the subgroups of the ATC classification, both in terms of price growth rates and in terms of the level of the weighted average price per pack, the G03 subgroup “Sex hormones and modulators of the reproductive system” is in the lead. In order to prevent higher rates of price growth in the studied market segment and ensure the availability of drug supply, it is proposed to conduct regular monitoring of price changes and, if an unfavorable development of the price situation is detected, make operational management decisions.

Understanding International Price and Consumption Disparities

AbstractThis study proposes a new measure of the tradability of 120+ commodities based on price dispersion. This approach is used to construct price indices of tradables and non‐tradables for 150+ countries. The expenditure share of tradables is lower for richer countries, while the relative price of non‐tradables, which plays an important role in the determination of real exchange rates, is higher. Secondly, a common‐factor approach (based on principal components) is introduced to compress the large volume of information on prices and quantities consumed globally. We find that cross‐commodity correlations are higher for prices than for consumption. In addition, income is responsible for most of the variation in the first principal component of consumption but explains less of the first price component. This suggests consumption are driven primarily by domestic factors, while prices are determined by factors outside the country, along the lines of the Purchasing Power Parity theory.