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

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.

scholarly journals Simulative Verification of the Possibility of using Multiple Regression Models for Real Estate Appraisal

2019 ◽  
Vol 27 (3) ◽  
pp. 109-123
Author(s):  
Sebastian Kokot ◽  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Multiple Regression ◽  
Regression Models ◽  
Negative Impact ◽  
Real Estate Market ◽  
Real Property ◽  
The Real ◽  
Real Estate Valuation ◽  
Multiple Regression Models

Abstract The possibility of using multiple regression models in real estate valuation is the subject of disputes, both in theory and in practice. Econometric modelling is a difficult process, since a number of issues of substantive and numerical nature occur during that process. Modern technologies enable quick and easy model estimation with the use of virtually any quality of data. Naturally, it provokes property appraisers to use such models in the practice of real property valuation, particularly in mass appraisal, frequently without taking those issues into account. Consequently, the models obtained and applied in practice turn out to be of poor quality and, objectively speaking, should not serve as the basis for determining real estate value. The specificity of the real estate market and of the real properties themselves as objects traded in that market additionally exert a negative impact on the quality of the obtained models. In this article, the authors present the results of research which involved a simulation of various types of disturbances of a model artificially developed database of real estate prices and attributes as well as their impact on the quality of estimated models. The research will make it possible to answer the question of the degree and type of disturbances that are permissible in the functioning of a real estate market if the estimated models are to still satisfy the qualitative requirements defined for them, and thereby produce accurate valuation results. A model database will be disturbed by the deviation of prices from model prices and by reducing its size. Radom generators were used to obtain database disturbances.

scholarly journals 242 Effect of trucking distance on sale price of beef calf and feeder cattle lots sold through video auctions from 2010 through 2018

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 10-11
Author(s):  
Esther D McCabe ◽  
Mike E King ◽  
Karol E Fike ◽  
Maggie J Smith ◽  
Glenn M Rogers ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Average Weight ◽  
Sale Price ◽  
Feeder Cattle ◽  
A Value ◽  
Multiple Regression Models ◽  
Determine Effect

Abstract The objective was to determine effect of trucking distance on sale price of beef calf and feeder cattle lots sold through Superior Livestock Video Auctions from 2010 through 2018. Data analyzed were collected from 211 livestock video auctions. There were 42,043 beef calf lots and 19,680 feeder cattle lots used in these analyses. Six states (Colorado, Iowa, Kansas, Nebraska, Oklahoma, and Texas) of delivery comprised 70% of calf lots and 83% of feeder cattle lots and were used in these analyses. All lot characteristics that could be accurately quantified or categorized were used to develop multiple regression models that evaluated effects of independent factors using backwards selection. A value of P < 0.05 was used to maintain a factor in the final models. Based upon reported state of origin and state of delivery, lots were categorized into one of the following trucking distance categories: 1) Within-State, 2) Short-Haul, 3) Medium-Haul, and 4) Long-Haul. Average weight and number of calves in lots analyzed was 259.2 ± 38.4 kg BW and 100.6 ± 74.3 head, respectively. Average weight and number of feeder cattle in lots analyzed was 358.4 ± 34.3 kg BW and 110.6 ± 104.1 head, respectively. Beef calf lots hauled Within-State sold for more ($169.24/45.36 kg; P < 0.0001) than other trucking distance categories (Table 1). Long-Haul calf lots sold for the lowest (P < 0.0001) price ($166.70/45.36 kg). Within-State and Short-Haul feeder cattle lots sold for the greatest (P < 0.0001) price ($149.96 and $149.81/45.36 kg, respectively; Table 2). Long-Haul feeder cattle lots sold for the lowest (P < 0.0001) price, $148.43/45.36 kg. These results indicate there is a price advantage for lots expected to be hauled shorter distances, likely because of cost and risk associated with transportation.

Multiple regression models for predicting total daily pollen concentration in Cartagena

Grana ◽  
2005 ◽  
Vol 44 (2) ◽  
pp. 108-114 ◽  
Author(s):  
José Manuel Angosto ◽  
Stella Moreno‐Grau ◽  
Javier Bayo ◽  
Belén Elvira‐Rendueles
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Pollen Concentration ◽  
Multiple Regression Models ◽  
Daily Pollen Concentration

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

Real estate development in the city of Athens during the financial crisis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria Nikitidou ◽  
Fragiskos Archontakis ◽  
Athanasios Tagkalakis
Keyword(s):  
Financial Crisis ◽  
Real Estate ◽  
Structural Characteristics ◽  
House Prices ◽  
Real Estate Market ◽  
Structural Factors ◽  
Data Set ◽  
Content Type ◽  
Capital Financing ◽  
The City

Purpose This study aims to determine how the prices of residential properties in the Greek real estate sector are affected by their structural characteristics and by the prevailing economic factors during recession. Design/methodology/approach Based on 13,835 valuation reports for the city of Athens, covering a period of 11 years (2006–2016), this study develops a series of econometric models, taking into account both structural characteristics of the property market and the macroeconomic relevant variables. Finally, the city of Athens is divided into sub-regions and the different effects of the structural factors in each area are investigated via spatial analysis confirming the validity of the baseline model. Findings Findings show that the size, age, level, parking and storage space can explain the property price movements. Moreover, the authors find evidence that it is primarily house demand variables (e.g. the annual average wage, the unemployment rate, the user cost of capital, financing constraints and expectations about the future course of the house market) that affect house prices in a statistically significant manner and with the correct sign. Finally, using a difference-in-differences approach, this study finds that an increase in house demand (on account of net migration) led to higher house prices in smaller and older than in larger and younger apartments in areas with high concentration of immigrants. Originality/value This study uses a novel data set to help entities, individuals and policy-makers to understand how the recent economic and financial crisis has affected the real estate market in Athens.

Abstract WP238: Atrial Fibrillation is Associated With Severe Basal Ganglia Perivascular Spaces

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Alvin S Das ◽  
Elif Gokcal ◽  
Robert W Regenhardt ◽  
Andrew Warren ◽  
Kristin Schwab ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Basal Ganglia ◽  
Multiple Regression ◽  
Regression Models ◽  
Small Vessel Disease ◽  
Perivascular Spaces ◽  
Nonvalvular Atrial Fibrillation ◽  
Related Factors ◽  
Multiple Regression Models ◽  
Independent Effects

Introduction: High burdens of basal ganglia-perivascular spaces (BG-PVS) are often attributed to underlying hypertensive cerebral small vessel disease (HTN-CSVD). Although PVS are thought to arise from decreased perivascular drainage related to changes in arterial pulsatility, the contribution of pulsatility changes from nonvalvular atrial fibrillation (NVAF) has not been studied. Hypothesis: We hypothesized that NVAF patients have a higher burden of BG-PVS than HTN-CSVD patients, possibly through hemodynamic factors related to NVAF. Methods: Through an observational single-center study of consecutive stroke patients, we compared BG-EPVS severity between 136 patients with NVAF-related ischemic stroke (NVAF-IS) and 107 patients with HTN-CSVD-related intracerebral hemorrhage (HTN-ICH) without NVAF. Within the NVAF cohort, we also built multiple regression models to evaluate independent effects of NVAF-related factors on BG-PVS. All multiple regression models were adjusted for age, hypertension, sex, and neuroimaging markers of CSVD (extent of white matter hyperintensities (WMH), presence of lacunes, and cerebral microbleeds). Results: Patients with NVAF-IS were older than patients with HTN-ICH (75 + 12 vs. 64 + 13, p < 0.0001); however, there was no difference in sex between groups ( p = 0.6). Severe BG-PVS (defined as > 20 PVS in the BG) were found in 42.6% of NVAF-IS patients vs. 8.4% of HTN-ICH ( p < 0.0001). Even after multivariate adjustment, the presence of NVAF remained significantly related to BG-PVS ( p = 0.001). Within the NVAF cohort, CHA2DS2-VASc was associated with the presence of severe BG-PVS ( p = 0.003) despite controlling for other covariates. When CHA2DS2-VASc was replaced with its individual components in the same regression model, congestive heart failure (CHF, p = 0.017), WMH burden ( p = 0.009), and age ( p = 0.02) were found to be predictors of severe BG-PVS. Conclusions: Severe BG-PVS were significantly more common in NVAF patients compared to HTN-CSVD patients. NVAF-related features (CHA2DS2-VASc score) and CHF were associated with higher burdens of BG-PVS. These findings suggest that NVAF might play a role in the development of BG-PVS, conceivably through hemodynamic factors.

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