Real Estate Risk: Appraisal Capture
Real estate has always been an important asset class. Massive investment in real estate since the financial crisis 2008 has pushed the demand for integrated and micro-based risk modeling. The data for such risk management is available to banks and other investors, but is not structured and therefore not model-ready. Appraisals are available as printouts as well as pdf files, but the relevant data is only available in semi-structured form and differs by appraisal. A central issue in integrated real estate risk management consists of transferring the relevant information from appraisals into a risk management environment without too much unnecessary human effort. The goal of this article is to describe an approach to identifying and extracting existing semi-structured appraisal information into structured information ready to be consumed by risk models and other applications.
KeywordsReal estate risk Real estate appraisal Natural language processing Tokenization and stemmers Named entity recognition NER Word2vec Table 2vec
- Deng, L. (2018). Table2Vec: Neural word and entity embeddings for table population and retrieval. Stavanger: University of Stavanger.Google Scholar
- Enzinger, P., & Grossmann, S. (2019). Managing internal and external network complexity. In V. Liermann & C. Stegmann (Eds.), The impact of digital transformation and fintech on the finance professional. New York: Palgrave Macmillan.Google Scholar
- Liermann, V., Li, S., & Schaudinnus, N. (2019). Deep learning—An introduction. In V. Liermann & C. Stegmann (Eds.), The impact of digital transformation and fintech on the finance professional. New York: Palgrave Macmillan.Google Scholar
- Liermann, V., Viets, N., & Grossmann, S. (2018, August). Retrieved September 19, 2018, from www.risknet.de https://www.risknet.de/themen/risknews/a-holistic-approach-to-real-estate-risk-management/5a179b5d33c31dd70fa3743aa91f23a6/.
- Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7, 77–91. Google Scholar
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Mountain View: Google Inc.Google Scholar
- NLTK Project. (2018, November 17). Natural language toolkit—Home. Retrieved from Natural Language Toolkit https://www.nltk.org/.