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2. Real-estate bubble - Wikipedia

   en.wikipedia.org/wiki/Real-estate_bubble

   Real estate bubbles are invariably followed by severe price decreases (also known as a house price crash) that can result in many owners holding mortgages that exceed the value of their homes. [ 32 ] 11.1 million residential properties, or 23.1% of all U.S. homes, were in negative equity at December 31, 2010. [ 33 ]

3. House price index - Wikipedia

   en.wikipedia.org/wiki/House_price_index

   FNC Inc. publishes the Residential Price Index based on data collected from public records blended with real-time appraisals of property and neighborhood attributes. The RPI is the mortgage industry's first hedonic price index for residential properties.

4. Real estate economics - Wikipedia

   en.wikipedia.org/wiki/Real_estate_economics

   Real estate economics is the application of economic techniques to real estate markets. It aims to describe and predict economic patterns of supply and demand . The closely related field of housing economics is narrower in scope, concentrating on residential real estate markets, while the research on real estate trends focuses on the business ...

5. Hedonic regression - Wikipedia

   en.wikipedia.org/wiki/Hedonic_regression

   Hedonic modeling was first published in the 1920s as a method for valuing the demand and the price of farm land. However, the history of hedonic regression traces its roots to Church (1939), [3] which was an analysis of automobile prices and automobile features. [4] Hedonic regression is presently used for creating the Consumer Price Index (CPI ...

6. Regression analysis - Wikipedia

   en.wikipedia.org/wiki/Regression_analysis

   In the more general multiple regression model, there are independent variables: = + + + +, where is the -th observation on the -th independent variable.If the first independent variable takes the value 1 for all , =, then is called the regression intercept.

7. Ordinary least squares - Wikipedia

   en.wikipedia.org/wiki/Ordinary_least_squares

   In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...