Parameterized Comparison of Regularized Regression Models to Develop Models for Real Estate

The experience of sadness is largely unpleasant, but when expressed through music, it can be pleasurable. Previous research has shown that an attraction to sad music is correlated with personality traits like empathy, Absorption, and rumination. However, the intricacies of the relationship between personality, situational factors, and reasons for engaging with sad music have yet to be fully explored. To address this, participants ( N = 431) reported the situations in which they would listen to sad music and their motivations for doing so. Regularized regression models were employed to assess correlations between personality, situational, and motivational factors. Mediation models were used to determine if emotional responses mediated these associations. People who scored higher on Absorption, the Fantasy component of empathy, and rumination reported enjoying sad music. Absorption and Fantasy were associated with liking sad music because of its ability to regulate/enhance positive emotions. Rumination was associated with liking sad music in tense situations because it both strengthens positive and releases negative emotions. Our results further our understanding of reward responses to negative stimuli by highlighting the role of personality and situational factors. Such findings have implications for the development of interventions for mood disorders, in which music could be used as a tool to regulate emotions and re-engage the reward system.

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Impact of the Regularization of Regression Models on the Results of the Mass Valuation of Real Estate

Folia Oeconomica Stetinensia ◽

10.2478/foli-2020-0009 ◽

2020 ◽

Vol 20 (1) ◽

pp. 163-176

Author(s):

Sebastian Gnat

Keyword(s):

Real Estate ◽

Multiple Regression ◽

Ridge Regression ◽

Regression Models ◽

Real Estate Market ◽

Data Set ◽

Important Conclusion ◽

Property Valuation ◽

Multiple Regression Models ◽

Training Sets

AbstractResearch background: Mass appraisal is a process in which multiple properties are appraised simultaneously, with a uniform approach. One of the tools that can be used in this area are multiple regression models. In the valuation of real estate features are often described on an ordinal or nominal scale. Replacing them with dummy variables with an insufficient number of observations leads to multicollinearity. On the other hand, there is a risk of overfitting the model. One of the ways to eliminate or weaken these phenomena is to introduce regularization based on a model’s penalization for the high values of its weights.Purpose: The aim of the study is to verify the hypothesis whether regularized regression reduces the errors of property valuation and which of the analyzed methods is the most effective in this context.Research methodology: The article will present a study in which two ways of regularization will be applied – ridge and lasso regression, in the context of their impact on the errors of property valuation. The analyzed data set includes over 300 land properties valued by property appraisers. The key aspects of the study are the selection of optimal values of the regularization parameter and its influence on model’s errors with a different number of observations in the training sets.Results: The study showed that regularization improves valuation results and, more specifically, allows for lower average absolute percentage errors. The improvement of model effectiveness was more pronounced in the case of ridge regression. An important result is also that regularization has provided a higher accuracy of valuation compared to multiple regression models for smaller training sets.Novelty: The article confirms the effectiveness of regularization as a way to eliminate the problem of multicollinearity or overfitting of the model. The results showed that ridge regression can be an effective way of modelling the value of real estate. Especially in the case of a small amount of market data, which is an important conclusion in the context of the real estate market.

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

Journal of Risk and Financial Management ◽

10.3390/jrfm13050105 ◽

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.

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Regularized regression models to predict the product quality in multistep manufacturing

2016 5th International Conference on Systems and Control (ICSC) ◽

10.1109/icosc.2016.7507067 ◽

2016 ◽

Cited By ~ 1

Author(s):

Mariam Melhem ◽

Bouchra Ananou ◽

Mustapha Ouladsine ◽

Jacques Pinaton

Keyword(s):

Product Quality ◽

Regression Models ◽

Regularized Regression

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Application of Statistical Methods to the Assessment of Prices of Klaipeda City Apartments

Lietuvos statistikos darbai ◽

10.15388/ljs.2014.13896 ◽

2014 ◽

Vol 53 (1) ◽

pp. 64-77

Author(s):

Roberta Navickaitė

Keyword(s):

Real Estate ◽

Regression Model ◽

Statistical Methods ◽

Nonlinear Regression ◽

Regression Models ◽

Additive Model ◽

Nonlinear Regression Model ◽

Generalised Additive Model ◽

Parametric Regression ◽

Real Estate Valuation

The paper analyses the use of a nonlinear regression model, generalised linear model and generalised additive model(semi-parametric regression model) for creating real estate valuation models. These models are applied to data on transactions inKlaipeda city apartments. The aim is to create real estate valuation regression models applying various statistical methods and tocompare them with each other. The practical aspects of creating regression models are analysed and conclusions are presented in thepaper.

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Statistical Seasonal Prediction Based on Regularized Regression

Journal of Climate ◽

10.1175/jcli-d-16-0249.1 ◽

2017 ◽

Vol 30 (4) ◽

pp. 1345-1361 ◽

Cited By ~ 9

Author(s):

Timothy DelSole ◽

Arindam Banerjee

Keyword(s):

Regression Models ◽

Linear Prediction ◽

Prediction Models ◽

Seasonal Prediction ◽

Statistical Prediction ◽

Dynamical Model ◽

Model Output ◽

Lasso Regression ◽

Regularized Regression ◽

Model Data

Abstract This paper proposes a regularized regression procedure for finding a predictive relation between one variable and a field of other variables. The procedure estimates a linear prediction model under the constraint that the regression coefficients have smooth spatial structure. The smoothness constraint is imposed using a novel approach based on the eigenvectors of the Laplace operator over the domain, which results in a constrained optimization problem equivalent to either ridge regression or least absolute shrinkage and selection operator (LASSO) regression, which can be solved by standard numerical software. In addition, this paper explores an unconventional procedure whereby regression models are estimated from dynamical model output and then verified against observations—the reverse of the traditional order. The methodology is illustrated by constructing statistical prediction models of summer Texas-area temperature based on concurrent Pacific sea surface temperature (SST). None of the regularized regression models have statistically significant skill when estimated from observations. In contrast, when estimated from dynamical model output, the regression models have skill with respect to dynamical model data because of the substantially larger sample size available from dynamical model output. In addition, the regression models estimated from dynamical model data can predict observed anomalies with significant skill, even though no observations were used directly to estimate the regression models. The results indicate that dynamical models had no significant skill because they could not accurately predict the SST itself, not because they could not capture realistic SST teleconnections.

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ANALYSIS OF DIFFERENT REGRESSION MODELS FOR REAL ESTATE PRICE PREDICTION

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i11.041 ◽

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.

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