skip to main content
10.1145/3368691.3368730acmotherconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

A framework of business intelligence solution for real estates analysis

Published: 02 December 2019 Publication History

Editorial Notes

NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the DATA 2019 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

Abstract

Real estate is one of the essential and challenging fields in the market which reflects the economy, and it needs constant improvement. Business intelligence nowadays plays a significant role in enhancing the process of decision making and risk management in many different fields. One of the promising fields is the real estate investment market. This paper proposes a framework for an effective BI solution for analyzing the real estate market and estimating the price of the properties. The building of the BI solution, which passes through multiple phases is demonstrated.

References

[1]
Kahr, J. and Thomsett, M.C. Real estate market valuation and analysis. John Wiley \& Sons, 2006.
[2]
Alfiyatin, A.N., Febrita, R.E., Taufiq, H. and Mahmudy, W.F. Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization. International Journal of Advanced Computer Science and Applications (2017).
[3]
Yu, H. and Wu, J. Real Estate Price Prediction with Regression and Classification. Stanford University, California, 2016.
[4]
Richards, G., Yeoh, W., Chong, A.Y.L. and Popovič, A. Business intelligence effectiveness and corporate performance management: an empirical analysis. Journal of Computer Information Systems, 59, 2 (2019), 188---196.
[5]
Jain, N., Goel, P., Sharma, P. and Deep, V. Prediction of House Pricing Using Machine Learning with Python. Available at SSRN 3403964 (2019).
[6]
Zaied, A.N.H., Grida, M.O. and Hussein, G.S. EVALUATION OF CRITICAL SUCCESS FACTORS FOR BUSINESS INTELLIGENCE SYSTEMS USING FUZZY AHP. Journal of Theoretical and Applied Information Technology, 96, 19 (2018).
[7]
Trieu, V.H. Getting value from Business Intelligence systems: A review and research agenda. Decision Support Systems, 93 (2017), 111--124.
[8]
Llave, M.R. Business Intelligence and Analytics in Small and Medium-sized Enterprises: A Systematic Literature Review. Procedia Computer Science, 121 (2017), 194--205.
[9]
Laursen, G.H. and Thorlund, J. Business analytics for managers: Taking business intelligence beyond reporting. John Wiley \& Sons, 2016.
[10]
Bahrami, M., Arabzad, S.M. and Ghorbani, M. Innovation in market management by utilizing business intelligence: introducing proposed framework. Procedia-Social and Behavioral Sciences, 41 (2012), 160--167.
[11]
Kasemsap, K. The Fundamentals of Business Intelligence. International Journal of Organizational and Collective Intelligence (IJOCI), 6, 2 (2016), 12--25.
[12]
Wazurkar, P., Bhadoria, R.S. and Bajpai, D. Predictive analytics in data science for business intelligence solutions. (2017), IEEE.
[13]
Arora, M. and Chakrabarti, D. Application of Business Intelligence: A Case on Payroll Management. (2013), In 2013 International Symposium on Computational and Business Intelligence. IEEE.
[14]
Tzang, S.W., Hung, C.H., Chang, C.P. and Tsai, Y.S. Commercial Real Estate Evaluation: The Real Options Approach. (2018), In International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. Springer, Cham.
[15]
Valverde, R. A business intelligence system for risk management in the real estate industry. International Journal of Computer Applications, 27, 2 (2011), 14--22.
[16]
Wedyawati, W. and Lu, M. Mining real estate listings using ORACLE data warehousing and predictive regression. (2004), Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration.
[17]
Ravikumar, A.S. Real Estate Price Prediction Using Machine Learning. National College of Ireland, 2016.
[18]
Selim, H. Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert systems with Applications, 36, 2 (2009), 2843--2852.
[19]
Abai, N.H.Z., Yahaya, J.H. and Deraman, A. User requirement analysis in data warehouse design: a review. Procedia Technology, 11 (2013), 801--806.
[20]
Wang, Y., Yu, S. and Xu, T. A user requirement driven framework for collaborative design knowledge management. Advanced Engineering Informatics, 33 (2017), 16--28.
[21]
Dennis, A., Wixom, B.H. and Roth, R.M. Systems analysis and design. John wiley \& sons, 2018.
[22]
Sherman, R. Business intelligence guidebook: From data integration to analytics. Newnes, 2014.
[23]
Ong, I.L., Siew, P.H. and Wong, S.F. A five-layered business intelligence architecture. Communications of the IBIMA (2014).
[24]
Saini, C. and Arora, V. Information retrieval in web crawling: A survey. (2016), 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).IEEE.
[25]
Golfarelli, M. and Rizzi, S. From Star Schemas to Big Data: 20$$+ $$ Years of Data Warehouse Research. (2018), Springer.
[26]
Cuzzocrea, A. Data warehousing and OLAP over big data: a survey of the state-of-the-art, open problems and future challenges. International Journal of Business Process Integration and Management, 7, 4 (2015), 372--377.
[27]
Daradi, S.A.M., Yusof, U.K. and Kader, N.I.B.A. Prediction of Housing Price Index in Malaysia Using Optimized Artificial Neural Network. Advanced Science Letters, 24, 2 (2018), 1307--1311.
[28]
Turvey, R. The economics of real property: an analysis of property values and patterns of use. Routledge, 2017.
[29]
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.

Cited By

View all
  • (2021)Business Intelligence Framework Design and Implementation: A Real-estate Market Case StudyJournal of Data and Information Quality10.1145/342266913:2(1-16)Online publication date: 30-Jun-2021

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
DATA '19: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems
December 2019
376 pages
ISBN:9781450372848
DOI:10.1145/3368691
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. business intelligence
  2. data modeling and architecture
  3. predictive analytics
  4. price estimation
  5. real estate

Qualifiers

  • Research-article

Conference

DATA'19

Acceptance Rates

DATA '19 Paper Acceptance Rate 58 of 146 submissions, 40%;
Overall Acceptance Rate 74 of 167 submissions, 44%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)23
  • Downloads (Last 6 weeks)4
Reflects downloads up to 03 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2021)Business Intelligence Framework Design and Implementation: A Real-estate Market Case StudyJournal of Data and Information Quality10.1145/342266913:2(1-16)Online publication date: 30-Jun-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media