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Search Results (4)

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Keywords = small real estate sample

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15 pages, 641 KB  
Article
Real Estate Valuations with Small Dataset: A Novel Method Based on the Maximum Entropy Principle and Lagrange Multipliers
by Pierfrancesco De Paola
Real Estate 2024, 1(1), 26-40; https://doi.org/10.3390/realestate1010003 - 31 Jan 2024
Cited by 7 | Viewed by 2724
Abstract
Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, [...] Read more.
Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, underscore the importance of adopting advanced approaches to obtain accurate valuations, especially with small property samples. The objective of this study is to explore the applicability of the Maximum Entropy Principle to real estate valuations with the support of Lagrange multipliers, emphasizing how this methodology can significantly enhance valuation precision, particularly with a small real estate sample. The excellent results obtained suggest that the Maximum Entropy Principle with Lagrange multipliers can be successfully employed for real estate valuations. In the case study, the average prediction error for sales prices ranged from 5.12% to 6.91%, indicating a very high potential for its application in real estate valuations. Compared to other established methodologies, the Maximum Entropy Principle with Lagrange multipliers aims to be a valid alternative with superior advantages. Full article
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16 pages, 1309 KB  
Article
Business Strategies and Competitive Advantage: The Role of Performance and Innovation
by Ida Farida and Doddy Setiawan
J. Open Innov. Technol. Mark. Complex. 2022, 8(3), 163; https://doi.org/10.3390/joitmc8030163 - 13 Sep 2022
Cited by 197 | Viewed by 204013
Abstract
This study aims to examine the effect of business strategies to improve the competitive advantages of small and medium enterprises (SMEs). Further, our study considers the importance of performance and innovation as mediating variables in the relationship between business strategies and competitive advantage. [...] Read more.
This study aims to examine the effect of business strategies to improve the competitive advantages of small and medium enterprises (SMEs). Further, our study considers the importance of performance and innovation as mediating variables in the relationship between business strategies and competitive advantage. The sample of the study consists of 150 SMEs in the construction and real estate industry. Our findings show that business strategies have a positive impact on competitive advantage. Better business strategies improve the competitive advantage of SMEs. Further, business performance and innovation also mediate the relationship between business strategies and competitive advantages. These results provide evidence of the importance of performance and innovation to improve the competitive advantage. It is suggested that SMEs improve their performance and innovation capability to strengthen their competitive advantages. Full article
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16 pages, 1093 KB  
Article
Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing
by Jose Torres-Pruñonosa, Pablo García-Estévez and Camilo Prado-Román
Mathematics 2021, 9(7), 783; https://doi.org/10.3390/math9070783 - 6 Apr 2021
Cited by 20 | Viewed by 3906
Abstract
We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log [...] Read more.
We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal. Full article
(This article belongs to the Special Issue Statistical Data Modeling and Machine Learning with Applications)
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17 pages, 401 KB  
Article
Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples
by Vincenzo Del Giudice, Pierfrancesco De Paola, Fabiana Forte and Benedetto Manganelli
Sustainability 2017, 9(11), 2138; https://doi.org/10.3390/su9112138 - 21 Nov 2017
Cited by 37 | Viewed by 6192
Abstract
This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Carlo Method (MCHMCM). On the same [...] Read more.
This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Carlo Method (MCHMCM). On the same real estate sample, MCHMCM has been compared with a neural networks model (NNs), traditional multiple regression analysis (MRA) and the Penalized Spline Semiparametric Method (PSSM). All four methods have been developed for testing the forecasting capacity and reliability of MCHMCM in the real estate field. The Markov Chain Hybrid Monte Carlo Method has proved to be the best model with an absolute average percentage error of 6.61%. Full article
(This article belongs to the Special Issue Real Estate Economics, Management and Investments)
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