Determinants Of Prices Research Articles

Econometric Advances in the Estimation of Housing Price Determinants

This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing prices also has complications related to using the OLS estimator with fixed effects when the data has serial correlation. We show these problems can be overcome using the Feasible Generalised Least Squares estimator in a Seemingly Unrelated Regression Equations (FGLS-SURE) system. Recent econometric developments produce more accurate housing price determinant estimates than conventional econometric methods. These new methodologies can help researchers better estimate the effects of fundamental economic changes and policy decisions on housing prices, which can, in turn, support policymakers in implementing better housing policies.

Determinants of housing prices: Serbian Cities’ perspective

This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.

Multilevel modelling, location and property valuations: an application to the Italian residential market

PurposeThe use of statistical methods in the field of real estate appraisals presents a trade-off between the efficiency of the estimates (that would require the use of sophisticate econometric models) and the ease of the economic interpretation of the outcomes (that characterises the hedonic pricing models). This paper shows that multilevel modelling (MLM) can represent a suitable solution to this trade-off.Design/methodology/approachThis paper uses the so-called “multilevel modelling” (henceforth MLM). MLM can represent a further step forward in the use of statistical methods in property appraisals. MLM is easy to implement and the MLM estimates have a clear economic meaning. Furthermore, MLM provides more efficient estimates of hedonic prices (the prices of housing attributes) with respect to standard hedonic pricing models. Finally, MLM is particularly suitable for the housing market analysis, where the feature “location” plays a key role.FindingsFor the Italian context, characterised by many “benchmark locations” (small municipalities that share similar geographic, historic, and socioeconomic characteristics), the paper finds that multilevel modelling (MLM) is needed to correctly estimate the hedonic prices also in a micro-area.Practical implicationsMLM allows to further enhance the key role of “location”. Location is indeed used as the “grouping variable” in MLM, instead of being treated as a generic housing attribute in hedonic pricing models. When the benchmark locations are many, therefore, MLM represents a very effective compromise between the estimates’ efficiency and the ease of outcomes’ economic interpretation.Originality/valueUnlike the related literature that, basically, use MLM to investigate what are the main determinants (levels) of housing prices, this paper uses MLM to make more efficient the estimation of hedonic prices.

Research on factors influencing housing prices in Beijing

This paper delves into the issue of soaring housing prices in Chinas real estate sector, with a particular focus on Beijing. Employing a multiple linear regression model, it conducts an empirical analysis to identify the factors influencing housing prices. Furthermore, it synthesizes scholarly perspectives to offer a comprehensive discussion on these influencing factors. The research highlights Beijings real estate development, residential investment, and residential sales area as pivotal determinants of housing prices. Building upon these findings, the study advocates for the implementation of effective measures such as land policy regulation and restrictions on real estate transactions to guide the real estate market development prudently and maintain housing prices within a rational range. Results show that real estate development residential investment has a significant positive impact on housing prices, while residential sales area has a significant negative impact. Ultimately, the research endeavors to furnish a scientific foundation for policy formulation and assess its impact, thereby fostering the healthy growth of the real estate market, ensuring stable economic progress, and enhancing the well-being of people.

Utilizing 31 Chinese province panel data models to investigate the factors influencing house prices

In the realm of data analysis, this research employs regression models to delve into the complexities of housing market dynamics. The construction sector of real estate development companies, the amount invested in real estate development, and the gross regional product have all been found to be important determinants of home prices. Interestingly, the most significant factor is the investment in real estate development, which has a significant impact on house prices. The analysis reveals a positive correlation between the gross regional product and investment amount in real estate development with housing prices, suggesting that as these economic indicators rise, so too do housing prices. Conversely, the author observes a negative correlation between the construction area of real estate development enterprises and housing prices, indicating that an increase in construction area is associated with a decrease in housing prices. These findings underscore the importance of considering these key factors when analyzing housing market trends, providing valuable insights for policymakers, investors, and researchers alike. By understanding these relationships, stakeholders can make more informed decisions to navigate the ever-evolving housing market landscape.

Market Integration and Price Transmission of Grain And Rice In Jepara Regency

Abstract Jepara is one of the rice-producing centers in Central Java. The price of rice in Jepara always experiences an increase, which is not always the same as changes in the price of grain at the producer level. The differences in price movements in the market vertically indicate the need to study integration analysis and vertical price transmission. This study aims to analyze the market integration and price transmission of grain and rice between producers and consumer in Jepara Regency. The data used is secondary data in the form of daily data on the real price of dry unhusked rice harvested at the farm level, the price of rice at the mill level and the price of rice at the consumer level in Jepara Regency from 2020 to 2022 from the Office of Agriculture and Plantation of Central Java Province and the Food Security and Agriculture Service, Jepara Regency. Data was analysed using the VECM (Vector Auto Correction Model). The results showed that there was a vertical price asymmetry in the short and long term between the price of grain at the producer level (GKP), the price of milled rice (BMP), the price of wholesale rice (HBG) and the price of retail rice (BME) in Jepara Regency. Price formation at the consumer or retail level is influenced by the price of rice at the wholesale level and the price of grain at the farm level. The price of grain at the farm level also affects the price of rice at the milling level and the wholesale level but not vice versa. Thus, it can be concluded that grain producers are the determinants of rice prices in Jepara Regency.

Determinants of stock price and company value viewed from corporate governance, leverage, profitability, EPS

Determinants of stock price and company value viewed from corporate governance, leverage, profitability, EPS

Does Idiosyncratic Risk Have a Significant Impact on Return Probability? A Case Study of Borsa Istanbul 100 Stocks

Most asset pricing models ignore idiosyncratic risk, or firm-specific risk, while it is one of the most critical determinants of asset pricing and stock returns. In this paper, we investigate the impact of idiosyncratic risk on the returns of stocks traded on the Borsa Istanbul using six different fixed effect panel tobit and four different fixed effect panel logit regression models. The results of logit models suggest that as idiosyncratic risk increases, probability of positive stock returns also increases. Furthermore, an increase in a stock's market sensitivity has a negative effect on the probability of positive returns, while an increase in the market-to-book ratio, firm size, and market return has positive effects on returns. In all models, the explanatory variables, including idiosyncratic risk, market-to-book ratio, firm size, and market return, have a positive effect on returns, except for the model where negative values of dependent variable are censored at zero.

The price of short-term housing: A study of Airbnb on 26 regions in the United States

This study investigates the influence of customer review content and other attributes on Airbnb listing prices, using data from listings across 26 U.S. regions. By integrating sentiment analysis and topic modeling with hedonic and quantile regression models, this paper examines the significant role of electronic word-of-mouth in the short-term rental market, by studying how review sentiments and topics affect pricing strategies. The findings reveal that negative reviews have twice the impact on listing prices as positive reviews; and fourfold for lower-priced listings. Moreover, the results of topic modeling show that both positive and negative reviews related to facilities and location have a larger impact on listing prices. Additionally, the study looks at how the distance of listings from the city center and the timing of reviews affect pricing adjustments. The influence of reviews diminishes for listings located farther from the city center, indicating that Airbnb listing prices near the city center are more sensitive to changes in price determinants. The study also uncovers the existence of a lag effect in how reviews impact prices, showing that Airbnb hosts may not adjust prices as quickly as hotel owners. Overall, the study enriches existing literature by taking into account the review content and type of customer reviews, offering a more comprehensive understanding of short-term rental pricing.

Determinants of Residential Real Estate Property Prices in Ethiopia: The Case of Bahir Dar City

The Ethiopian real estate industry has grown in the previous two decades, especially in the capital city of the country and regional capitals, including Bahir Dar. Real estate prices have doubled and even tripled in a few years because of different factors. Thus, the purpose of this study is to analyse the factors affecting residential real estate prices in Ethiopia, particularly in Bahir Dar City. For this purpose, the study used both purposive and stratified random sampling techniques. Descriptive and inferential statistics were used to analyze the data, and the ordinary least squares method was used to identify the factors that influence residential real estate prices. The hedonic regression model result reveals that R<sup>2</sup> statistics (0.880) indicate that 88% of the variation in residential real estate prices can be explained by variation in the independent variables. Hedonic regression model results revealed that plot size, floor area, number of rooms, age of the house, external wall finishing material, ceiling finishing material, the direction of the house, distance of the property from the main road, and involvement of brokers in the transaction process, were major micro-factors that had a significant effect on residential real estate prices at the 5% level of significance. Furthermore, the interview results show that the challenges faced by real estate developers mostly include loans and aid issues, high-interest rates, mortgage restrictions, the financial capacity of real estate companies, delays in plan approvals, and rising costs of construction materials are among the major challenges affecting investment in real estate. The study suggests that the municipality of Bahir Dar City should provide an adequate supply of land to real estate developers, improve infrastructure development, and federal and regional governments should provide the credit lending mechanism by lowering interest rate especially for low-income class.

Real Estate Price Prediction using ANN

This report explores the use of machine learning to predict real estate prices, addressing challenges such as market dynamics and data complexity. Through extensive data analysis and feature engineering, the study implements decision trees, random forests, and neural networks to capture the intricate factors affecting property values. The performance of these models is rigorously evaluated, providing stakeholders with reliable forecasts and insights into key price determinants. This research enhances decision-making in the real estate sector by improving the accuracy and transparency of price predictions. A Graphical User Interface is also developed to enter required information and display the predicted price. Keywords—Machine Learning, Price Prediction, Artificial Neural Network

Determinants of stock prices in telecommunication industry: An application of fundamental analysis

The present study attempted to assess the impact of fundamental ratios on the share prices of selected telecommunication companies in India. India has dramatically expanded over the past ten years to become the second-biggest telecoms market worldwide, with 1.17 billion users. The Indian telecom industry has proliferated thanks in part to the government of India’s liberal and reformist policies and strong customer demand. It has become a lucrative investment sector for investors due to its recent and prospective growth. Data on 13 telecom firms indexed in the S&P BSE telecommunication index from 2013 to 2022 were taken from companies’ annual reports, the BSE website (Bombay Stock Exchange), and other secondary sources. Six firm-specific fundamental factors viz. Debt to Equity ratio (D/E), Current ratio (CR), Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to earnings ratio (P/E), Return on equity (ROE), and three country-specific fundamental factors viz. Gross Domestic Product, Inflation rate, and S&P BSE Sensex return were considered. Fixed effect panel regression through Generalized Least Square (GLS) model was performed to find inferences. Debt Equity ratio and Inflation rate were found to impact share price negatively. Conversely, the Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to Earnings ratio (P/E), and Return on Equity (ROE) positively impacted selected companies’ share prices. The study results will benefit individual & institutional investors in formulating their investment and portfolio diversification strategies for gaining a high effective rate of return on their investments.

Determinants of Price of Stock of Nepalese Commercial Banks

Determinants of stock price of Nepalese commercial banks are examined. Primary goal of the study is to analyze, and evaluate the share price fluctuations of Nepalese commercial banks. A descriptive and causal comparative research design was utilized to achieve the objectives of this research. Secondary data will be gathered from the annual reports of sample seven commercial banks, the NEPSE, and other related companies. The MPS has a positive relationship with EPS, P/E ratio, and BVPS but a negative relationship with DPS. It is found that there is a strong positive association between all independent variables (EPS, DPS, BVPS, and P/E Ratio) and the dependent variable (MPS) Examined are the factors that affect Nepalese commercial banks' stock prices. The analysis and evaluation of Nepalese commercial banks' share price fluctuations is the study's main objective. The MPS has a negative relationship with DPS but a positive relationship with EPS, P/E ratio, and BVPS. The study reveals that during the study period of the Nepalese commercial banks, MPS appears to move the greatest, while price earnings ratio fluctuates the least. The study period's findings also shown a significant positive correlation between commercial banks' stock prices and their EPS, DPS, P/E ratios, and BVPS. The EPS, DPS, P/E ratio, and BVPS have a considerable positive correlation with the share price of commercial banks.

Exploring farmland price determinants in Northern Italy using a spatial regression analysis

Using spatial regression models, we detect determinants of farmland’s prices in a rural area located in the upper Treviso plain (Veneto region, Italy). Econometric analysis is based on a Spatial linear regression model able to account for spatial lags in the data. Estimates show which intrinsic and extrinsic characteristics have the greatest influence on price, and how buyers and sellers’ profiles also matter on the price determination. Our application fosters spatial regression models in rural real estate market analysis and appraisal, and highlights that in the area under study the farmland’s prices are significantly affected by factors that are rarely considered in the literature, such as sellers and buyers’ profiles, the land use in the context where the sold plot is located matters, the hydraulic risk of the area and the presence of large infrastructures.

How do wineries price their wine experiences?

PurposeThis paper aims to model the price of cellar tours using a hedonic pricing approach. The authors analyze the complex relationship between the price of an add-on (here, cellar tours) and the price of the reference product (here, wine).Design/methodology/approachThanks to a large database containing information on about 1,000 winery experiences, the authors regress the price of cellar tours on wine prices and on a broad set of objective characteristics that are (1) tour specific and (2) common to all tours offered by the winery. These exogenous controls include the type and style of experience offered, amenities and winemaking characteristics.FindingsThe authors show that the price of cellar tours follows the price of the most expensive wine sold by the winery, which is a proxy for reputation. The authors find that one of the main determinants of cellar tour prices is visit length: wineries charge more for longer experiences. The number of wines tasted during the visit also increases the price. Prices are higher in places where there is a high level of wine tourism activity, which might be a sign of authenticity.Practical implicationsWine producers in different countries need to gain insights on how to price cellar tours, which are composite goods. The results can help practitioners price their winery experience according to common practices in different wine regions. The results may also be of interest to professionals in the tourism sector who are in charge of the pricing of by-products (e.g. tee-shirts, books, etc.), or for luxury fashion labels extending their brand in the catering industry with cafes and restaurants.Originality/valueTo the best of the authors’ knowledge, this paper is the first empirical analysis that examines the complex relationship between the price of an add-on and the price of the reference product in the context of wine tourism.

Focal Points in Recent Tourism Price Research

The article aims to provide a comprehensive overview of recent academic studies on pricing in tourism and to deepen the understanding of the recently prevailing research streams on this topic. This article reviews research articles on tourism pricing published in journals in the Scopus database between 2017 and early 2023. An analysis of the selected literature identifies three research streams on pricing: research on price determinants, price development and customers’ price decisions. In terms of methodology, empirical studies predominate, and in terms of period, the distribution over the years shows a stable and consistent level of research activity. The article is essential for further research in the field by presenting a synthesis of recent academic work.

Financial Performance, Dividend Policy, and Firm Value in Indonesian Consumer Goods Sector: 2019-2021 Study

This study investigates the nexus between return on assets (ROA), return on equity (ROE), dividend payout ratio (DPR), and firm value, represented by stock prices, within the consumer goods industry sector on the Indonesia Stock Exchange from 2019 to 2021. Utilizing a sample of 26 companies, employing Partial Least Square (PLS) analysis, the research reveals that neither ROA nor ROE significantly influences stock prices, and ROE exhibits no impact on DPR. Moreover, DPR fails to influence stock prices, and it cannot serve as an intervening variable between ROA or ROE and stock prices. These findings suggest a decoupling between financial performance metrics and market valuation within the consumer goods sector, urging a reconsideration of conventional wisdom regarding the determinants of stock prices in this industry Highlights : Limited Influence of ROA and ROE: The study reveals that return on assets (ROA) and return on equity (ROE) do not significantly impact stock prices in the consumer goods sector. Mediation Role of DPR: Dividend payout ratio (DPR) fails to mediate the relationship between ROA or ROE and stock prices, indicating its limited influence on market valuation within the sector. Implications for Market Valuation: The findings challenge traditional assumptions about the determinants of stock prices in the consumer goods industry, prompting a reevaluation of financial performance metrics' relevance in market valuation. Keywords: Consumer Goods Sector, Stock Prices, ROA, ROE, DPR

Multi-factor stock trading strategy based on DQN with multi-BiGRU and multi-head ProbSparse self-attention

Reinforcement learning is widely used in financial markets to assist investors in developing trading strategies. However, most existing models primarily focus on simple volume-price factors, and there is a need for further improvement in the returns of stock trading. To address these challenges, a multi-factor stock trading strategy based on Deep Q-Network (DQN) with Multi-layer Bidirectional Gated Recurrent Unit (Multi-BiGRU) and multi-head ProbSparse self-attention is proposed. Our strategy comprehensively characterizes the determinants of stock prices by considering various factors such as financial quality, valuation, and sentiment factors. We first use Light Gradient Boosting Machine (LightGBM) to classify turning points for stock data. Then, in the reinforcement learning strategy, Multi-BiGRU, which holds the bidirectional learning of historical data, is integrated into DQN, aiming to enhance the model’s ability to understand the dynamics of the stock market. Moreover, the multi-head ProbSparse self-attention mechanism effectively captures interactions between different factors, providing the model with deeper market insights. We validate our strategy’s effectiveness through extensive experimental research on stocks from Chinese and US markets. The results show that our method outperforms both temporal and non-temporal models in terms of stock trading returns. Ablation studies confirm the critical role of LightGBM and multi-head ProbSparse self-attention mechanism. The experiment section also demonstrates the significant advantages of our model through the presentation of box plots and statistical tests. Overall, by fully considering the multi-factor data and the model’s feature extraction capabilities, our work is expected to provide investors with more precise trading decision support.Graphical abstract

지역여건을 고려한 아파트 경매의 매각가격 결정요인

This study empirically analyzes the determinants of apartment sale prices in the auction market, focusing on apartments auctioned in Daegu as of 2021. Specifically, it aims to examine whether various factors based on regional characteristics influence sale prices and to explore the detailed impact of residential density. The main empirical findings are as follows. Auction characteristics were all significant. Forced auctions and the number of bidders increased sale prices, while the number of unsuccessful bidders and unpaid management fees decreased sale prices. In terms of Characteristics of goods, the construction period, parking spaces per household, and small and medium-sized area showed significant results. The elapsed years reduced sale prices, while parking spaces per household and small and medium-sized area increased sale prices. In terms of location characteristics, subway distance and school distance were significant. However, it was analyzed that the influence on sales prices was very low. Finally, the study looked at the impact of residential amenities, which hadn't been considered before. Over half of the sub-variables showed significance. Dense residential areas were found to increase sale prices, indicating a preference for housing with various amenities. The results of this study confirmed the determinants of apartment auction sale prices. Specifically, it was established that dense residential areas yield high sale prices in the auction market, attributed to their relatively superior amenities. This study suggests that equal amenities are needed to stabilize the prices of local housing markets.

The Determinants of Prices of Fan Tokens as a New Sports Finance Tool

In the Covid-19 era, the income sources of sports clubs decreased, but the importance of fan tokens as new communication, management, and income source for sports clubs increased with Blockchain technology. For this reason, the relationships between fan token prices and club success, transaction volume, and attractiveness factors were examined and the important determinants of fan token prices were tried to be revealed. To determine the most suitable methods for panel data model, first of all, cross-section dependence, homogeneity, and unit root tests were carried out. Then the durbin test was performed and it was concluded that the variables were cointegrated. Finally, the coefficients were examined with the PMG/ARDL test and it was observed that the most important determinants of fan token prices were club success and transaction volume, respectively. While these factors affect fan token prices positively, the effect of the attractiveness factor is seen as less important and negative compared to other factors. In addition, in the error correction model established, the existence of a long-term equilibrium relationship between the variables was confirmed. It was concluded that the short-term deviations returned to the equilibrium after approximately 21 days.