scholarly journals Additive hedonic regression models for the Austrian housing market

2012 ◽  
Keyword(s):  
Housing Market ◽  
Regression Models ◽  
Hedonic Regression

Adelaide's Heart Transplant, 1970–88: 2. The ‘Transfer’ of Value within the Housing Market

1992 ◽  
Vol 24 (3) ◽  
pp. 323-339 ◽  
Author(s):  
B A Badcock
Keyword(s):  
Built Environment ◽  
Housing Market ◽  
Urban Space ◽  
Capital Flows ◽  
Regression Models ◽  
House Prices ◽  
Wealth Accumulation ◽  
Urban Restructuring ◽  
Value Transfer ◽  
Differential Movement

Regression analysis is used to examine the interaction of a number of processes that are thought to be responsible for the geographical transfer of value within the built environment. These are derived from an account by Smith of the restructuring of urban space. The ‘transfer’ of value is imputed from the differential movement of house prices between 1970 and 1988 for geographical submarkets within the Adelaide Metropolitan Area. Although the interpretation of the regression models is complicated, the evidence for a tilting of the ‘value transfer’ gradient from an inner-outer bias, to an outer—inner bias, can be statistically inferred from the processes of restructuring that have redirected capital flows within the built environment of Australian cities such as Adelaide, Sydney, and Melbourne in the course of the last two decades. Thus the uneven capital formation that characterises urban restructuring and is ultimately capitalised into real changes in house prices is a significant source of the added wealth that is accumulated from homeownership. By this means it is possible to bridge the two ‘islands’ of theory: Smith's account of urban restructuring and Saunders's concern with the sources of wealth accumulation within the housing market.

scholarly journals Estimating Bargaining Power in Real Estate Pricing Models: Conceptual and Empirical Issues

2020 ◽  
Vol 13 (5) ◽  
pp. 105
Author(s):  
Steven B. Caudill ◽  
Franklin G. Mixon
Keyword(s):  
Real Estate ◽  
Bargaining Power ◽  
Regression Models ◽  
Hedonic Regression ◽  
Omitted Variables ◽  
Pricing Models ◽  
The Real Estate ◽  
Real Estate Prices ◽  
Relative Bargaining Power ◽  
Real Estate Economics

The relative bargaining power of the buyer and seller is a key feature of real estate pricing models. Classic real estate studies have sought to address bargaining effects in hedonic regression models. Prior research proposes a procedure to estimate bargaining effects in hedonic regression models that depends critically on a substitution to eliminate omitted variables bias. This study shows that the proposed solution that is often cited in the real estate economics literature does not solve the omitted variables problem given that both models are merely different parameterizations of the same model, and thus produces biased estimates of bargaining power when certain property characteristics are omitted. A classic hedonic regression model of real estate prices using Corsican apartment data supports our contention, even when the assumption of bargaining power symmetry is relaxed.

scholarly journals Hedonic regression models for Tokyo condominium sales

2016 ◽  
Vol 60 ◽  
pp. 300-315 ◽  
Author(s):  
W. Erwin Diewert ◽  
Chihiro Shimizu
Keyword(s):  
Regression Models ◽  
Hedonic Regression

scholarly journals ANALYSIS OF DIFFERENT REGRESSION MODELS FOR REAL ESTATE PRICE PREDICTION

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Pranav Kangane ◽  
Aadesh Mallya ◽  
Aayush Gawane ◽  
Vivek Joshi ◽  
Shivam Gulve
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Regression Models ◽  
Mean Squared Error ◽  
House Prices ◽  
Absolute Error ◽  
Performance Comparison ◽  
Squared Error ◽  
Estate Property ◽  
Market Strategies

The housing market is a standout amongst the most engaged with respect to estimating the price and continues to vary. Individuals are cautious when they are endeavoring to purchase another house with their financial plan and market strategies. Consequently, making the housing market one of the incredible fields to apply the ideas of machine learning on how to enhance and anticipate the house prices with precision. The objective of the paper is the prediction of the market value of a real estate property and present a performance comparison between various regression models applied. Nine algorithms were selected to predict the dependent variable in our dataset and then their performance was compared using R2 score, mean absolute error, mean squared error and root mean squared error. Moreover, this study attempts to analyze the correlation between variables to determine the most important factors that are bound to affect the prices of house.

Determinants of housing prices in Baku: empirical analyses

2019 ◽  
Vol 12 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Khatai Aliyev ◽  
Mehin Amiraslanova ◽  
Nigar Bakirova ◽  
Narmin Eynizada
Keyword(s):  
Housing Market ◽  
Regression Models ◽  
Polynomial Regression ◽  
Housing Prices ◽  
Ordinary Least Squares ◽  
Real Estate Market ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting

Purpose This paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic. Design/methodology/approach Based on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares. Findings Location, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas. Research limitations/implications The data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered. Practical implications Estimation results can be used for housing valuation by real estate market participants and investors. Social implications Research findings reveal importance of bill of sale as a major price determinant and expected to attract policymakers’ attention to solve such a big social problem. Additionally, models can be based for price estimations in Baku housing market. Originality/value The study contributes to the literature by empirically analyzing housing market in Baku, Azerbaijan. Research produces new practically valuable findings.

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