Price forecasting for real estate using machine learning: A case study on Riyadh city

2021 ◽  
Author(s):  
Ali Louati ◽  
Rahma Lahyani ◽  
Abdulaziz Aldaej ◽  
Abdullah Aldumaykhi ◽  
Saad Otai
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Price Forecasting ◽  
Riyadh City

scholarly journals Sensitivity Analysis of Machine Learning Models for the Mass Appraisal of Real Estate. Case Study of Residential Units in Nicosia, Cyprus

2019 ◽  
Vol 11 (24) ◽  
pp. 3047 ◽  
Author(s):  
Thomas Dimopoulos ◽  
Nikolaos Bakas
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Real Estate ◽  
Daily Practice ◽  
Learning Approaches ◽  
Residential Units ◽  
The Cost ◽  
Automated Valuation ◽  
Further Development

A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer.

Challenges in Machine Learning for Document Classification in the Real Estate Industry

2019 ◽  
Author(s):  
Björn-Martin Kurzrock ◽  
Mario Bodenbender
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Document Classification ◽  
Real Estate Industry ◽  
The Real ◽  
The Real Estate

MULTI AREA LOAD FREQUENCY CONTROL OF A HYBRID POWER SYSTEM WITH ADVANCED MACHINE LEARNING CONTROLLER: A CASE STUDY OF ANDHRA PRADESH

2018 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
SHAREEF SYED MAHABOOB ◽  
SURESH CH.V. ◽  
NAGARAJU S.SIVA ◽  
◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Andhra Pradesh ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Load Frequency

HIGH-RESOLUTION ELECTROFACIES USING MACHINE LEARNING TO DECRYPT HIGH PORO-PERM ZONES IN CYCLIC SUCCESSION OF PLIOCENE BIOCLASTIC LIMESTONE: CASE STUDY FROM MADURA STRAIT AREA, EAST JAVA BASIN

2020 ◽  
Author(s):  
Sarvagya Parashar ◽  
◽  
Ivan Wu Zhia Ming ◽  
Iko Sagala ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution

scholarly journals Frequency Response Analysis Interpretation using Numerical Indices and Machine Learning: A Case Study based on a Laboratory Model

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Regelii S. de A. Ferreira ◽  
Patrick Picher ◽  
Hassan Ezzaidi ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Frequency Response ◽  
Laboratory Model ◽  
Response Analysis ◽  
Frequency Response Analysis

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor

2021 ◽  
Vol 11 (13) ◽  
pp. 5826
Author(s):  
Evangelos Axiotis ◽  
Andreas Kontogiannis ◽  
Eleftherios Kalpoutzakis ◽  
George Giannakopoulos
Keyword(s):  
Machine Learning ◽  
Coastal Zone ◽  
Extraction Process ◽  
Asia Minor ◽  
Efficiency And Effectiveness ◽  
Effectiveness And Efficiency ◽  
Intelligent Tools ◽  
Southern Balkans

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, and the different quality of language use across sources present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research aimed at the southern Balkans and the coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “expert–apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a machine learning (ML) algorithm, utilizing a combination of active learning (AL) and reinforcement learning (RL), and the expert is the human researcher. ML-powered research improved the effectiveness and efficiency of the domain expert by 3.1 and 5.14 times, respectively, fetching a total number of 420 relevant ethnopharmacological documents in only 7 h versus an estimated 36 h of human-expert effort. Therefore, utilizing artificial intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

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