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What Is Property Future Value & Mortgage Value Assessment?

Property value and mortgage value assessment is the estimation of the future value of a particular asset for purchase, insurance, and more.

Unlike most other purchases, real estate tends to rise in value over time. This rise is influenced by economic and social trends, environmental conditions, and on governmental controls and regulations. These large-scale trends directly affect the general demand for property ownership, the scarcity of property for purchase, the utility of the property for potential owners, and the ease at which property rights can be transferred.

Details

Why Is It Important to Have a Good Property Future Value & Mortgage Value Assessment Model?

Those in real estate and related industries like insurance and banking depend on accurate property value assessments. With the importance of these valuations and the multitude of factors influencing them, advanced machine learning (ML) methods should be employed as they yield more precise estimates than traditional valuation methods.

What Internal Data Should I Have for a Good Property Future Value & Mortgage Value Assessment Model?

In order to train your model, you need data that includes real estate transactions. All features of a property should be included, from age of structures on the property, how energy efficient the structures are, and demographic data on the neighborhood.

What External Data is Essential for a Good Model?

Essential external data for a good property future value and mortgage value assessment model are economic and social trends, governmental regulations and controls, and environmental factors.

What External Data May Prove Useful for a Good Model?

Macroeconomic indicators in the model to account for general trends in the market at a specific time (GDP for example) will improve your model.

What Are the Main Challenges of the Property Future Value & Mortgage Value Assessment Use Case?

There are two main challenges of property value assessment. Firstly, each property has unique features, from the size of the plot of land to the amenities within buildings on the land. Secondly, relevant, high quality, and timely real estate data remain an expensive input.

Interesting Case Studies and Blogs to Look Into

Yalantis: Predicting House Price Using Regression Algorithm for Machine Learning
All Wave AV: How AI & ML will transform the future of Real Estate Market | System Integrator & AV Solutions Provider

Tangible Examples of Impact

“Housing data over the past month continued to show a strong “V-shape” rebound, helping drive the broader economy. Existing home sales in July jumped to 5.86 million annualized units, a pace not seen since 2006. While this was in line with our expectations, we previously expected a pullback to follow in August. However, July pending sales, which lead existing home closings by 30-45 days, rose 5.9 percent, and purchase mortgage applications were of a similar magnitude in August as in July. New construction measures also showed strength, with July housing starts hitting the highest level since February. We now forecast residential fixed investment (RFI) in the third quarter to grow 48.7 percent annualized and have substantially upgraded our forecasts for both new and existing home sales.”

Fannie Mae: Rebound Continues as Housing Helps Lead Recovery

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