Financial Forecasting of the Real Estate Market

Dear Colleagues,

We are excited to announce a Special Issue of the Journal of Risk and Financial Management, focusing on the critical theme of “Financial Forecasting of the Real Estate Market”. In an era of evolving economic landscapes and dynamic market conditions, understanding and accurately predicting trends in the real estate sector are paramount and crucial to sustainable economic development and financial sector stability.

This Special Issue aims to bring together cutting-edge research, innovative methodologies, and insightful perspectives to shed light on the intricacies of financial forecasting within the realm of real estate. We invite contributions from scholars and practitioners across disciplines to explore, analyze, and debate various aspects of financial forecasting in real estate markets, including, but not limited to, the following:

• Novel predictive models and methodologies for real estate market forecasting;
• Behavioral finance and psychological factors influencing real estate market predictions;
• Impacts of economic indicators, policy changes, and geopolitical events on real estate forecasting;
• Risk management strategies and uncertainty quantification in real estate financial forecasting;
• Comparative studies of forecasting accuracy across different real estate markets and asset classes;
• The spillover effect of multiple markets in real estate forecasting;
• Selling mechanism and players’ games contributing to real estate market forecasting;
• Practical implications for investors, policymakers, and industry professionals based on forecasting insights;
• Integration of machine learning, artificial intelligence, and big data analytics in real estate forecasting;
• Ethical considerations and challenges in real estate financial forecasting research.

We welcome both theoretical and empirical contributions that advance our understanding of financial forecasting in real estate markets. Submissions should demonstrate academic rigor, methodological soundness, and relevance to contemporary issues in the field.

Do not miss this opportunity to contribute to the vibrant scholarly discourse on financial forecasting in the real estate market. Join us in shaping the future of real estate research and practice!

Dr. Zhi Dong
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• real estate forecasting
• real estate price and performance
• financial economics
• behavioral finance and economics
• economic policies
• forecasting accuracy
• selling mechanism
• risk management
• machine learning
• artificial intelligence
• big data