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Federal Housing Finance Agency - One of the best experts on this subject based on the ideXlab platform.

  • trend federal Housing finance agency house price index house price index all transactions state new york seasonally adjusted non seasonally adj 1975 1 2017 2 data planet statistical datasets by conquest systems inc dataset id 057 001 001
    2017
    Co-Authors: Federal Housing Finance Agency
    Abstract:

    Federal Housing Finance Agency. House Price Index: House Price Index - All Transactions | State: New York | Seasonally Adjusted: Non-Seasonally Adj, 1975/1 - 2017/2. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 057-001-001 Dataset: Presents an index (1st quarter 1980=100) of US single-family home prices, by state. All transactions include new purchases and refinancing of conforming, conventional mortgages purchased or securitized by Fannie Mae or Freddie Mac. Only mortgage transactions on single-family properties are included. Conforming refers to a mortgage that both meets the underwriting guidelines of Fannie Mae or Freddie Mac and that does not exceed the conforming loan limit. The House Price Index (HPI) is a broad measure of the movement of single-family house prices. The HPI is published by the Federal Housing Finance Agency (FHFA) using data provided by Fannie Mae and Freddie Mac. The Office of Federal Housing Enterprise Oversight (OFHEO), one of FHFA’s predecessor agencies, began publishing the HPI in the fourth quarter of 1995. The HPI is based on transactions involving conforming, conventional mortgages purchased or securitized by Fannie Mae or Freddie Mac. Only mortgage transactions on single-family properties are included. Conforming refers to a mortgage that both meets the underwriting guidelines of Fannie Mae or Freddie Mac and that does not exceed the conforming loan limit. Conventional mortgages are those that are neither insured nor guaranteed by the FHA, VA, or other federal government entities. Mortgages on properties financed by government-insured loans, such as FHA or VA mortgages, are excluded from the HPI, as are properties with mortgages whose principal amount exceeds the conforming loan limit. Mortgage transactions on condominiums, cooperatives, multi-unit properties, and planned unit developments are also excluded. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. http://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx Category: Housing and Construction, Prices, Consumption, and Cost of Living Subject: Single-Family Housing, Housing Market, Home Prices, Price Indexes Source: Federal Housing Finance Agency The Federal Housing Finance Agency (FHFA) was created on July 30, 2008 by the Housing and Economic Recovery Act of 2008. The agency's primary function is to regulate of the U.S. secondary mortgage market, including oversight of Fannie Mae, Freddie Mac, the 12 Federal Home Loan Banks. http://www.fhfa.gov/

  • trend federal Housing finance agency house price index house price index all transactions 1975 1 2016 1 data planet statistical datasets by conquest systems inc dataset id 057 001 001
    2016
    Co-Authors: Federal Housing Finance Agency
    Abstract:

    Federal Housing Finance Agency. House Price Index: House Price Index - All Transactions, 1975/1 - 2016/1. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 057-001-001 Dataset: Presents an index (1st quarter 1980=100) of US single-family home prices, by state. All transactions include new purchases and refinancing of conforming, conventional mortgages purchased or securitized by Fannie Mae or Freddie Mac. Only mortgage transactions on single-family properties are included. Conforming refers to a mortgage that both meets the underwriting guidelines of Fannie Mae or Freddie Mac and that does not exceed the conforming loan limit. The House Price Index (HPI) is a broad measure of the movement of single-family house prices. The HPI is published by the Federal Housing Finance Agency (FHFA) using data provided by Fannie Mae and Freddie Mac. The Office of Federal Housing Enterprise Oversight (OFHEO), one of FHFA’s predecessor agencies, began publishing the HPI in the fourth quarter of 1995. The HPI is based on transactions involving conforming, conventional mortgages purchased or securitized by Fannie Mae or Freddie Mac. Only mortgage transactions on single-family properties are included. Conforming refers to a mortgage that both meets the underwriting guidelines of Fannie Mae or Freddie Mac and that does not exceed the conforming loan limit. Conventional mortgages are those that are neither insured nor guaranteed by the FHA, VA, or other federal government entities. Mortgages on properties financed by government-insured loans, such as FHA or VA mortgages, are excluded from the HPI, as are properties with mortgages whose principal amount exceeds the conforming loan limit. Mortgage transactions on condominiums, cooperatives, multi-unit properties, and planned unit developments are also excluded. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. http://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx Category: Housing and Construction, Prices, Consumption, and Cost of Living Subject: Single-Family Housing, Housing Market, Home Prices, Price Indexes Source: Federal Housing Finance Agency The Federal Housing Finance Agency (FHFA) was created on July 30, 2008 by the Housing and Economic Recovery Act of 2008. The agency's primary function is to regulate of the U.S. secondary mortgage market, including oversight of Fannie Mae, Freddie Mac, the 12 Federal Home Loan Banks. http://www.fhfa.gov/

Jae Kwon Bae - One of the best experts on this subject based on the ideXlab platform.

  • using machine learning algorithms for Housing price prediction
    Expert Systems With Applications, 2015
    Co-Authors: Byeonghwa Park, Jae Kwon Bae
    Abstract:

    Housing price valuation is one of most important trading decisions.This study uses machine learning to develop Housing price prediction models.This study analyzes the Housing data of 5359 townhouses in Fairfax County, VA.The 10-fold cross-validation was applied to C4.5, RIPPER, Bayesian, and AdaBoost.RIPPER outperformed these other Housing price prediction models in all tests. House sales are determined based on the Standard & Poor's Case-Shiller home price indices and the Housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US Housing market. In addition to these Housing price indices, the development of a Housing price prediction model can greatly assist in the prediction of future Housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a Housing price prediction model. To improve the accuracy of Housing price prediction, this paper analyzes the Housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a Housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naive Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved Housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of Housing price prediction.

Byeonghwa Park - One of the best experts on this subject based on the ideXlab platform.

  • using machine learning algorithms for Housing price prediction
    Expert Systems With Applications, 2015
    Co-Authors: Byeonghwa Park, Jae Kwon Bae
    Abstract:

    Housing price valuation is one of most important trading decisions.This study uses machine learning to develop Housing price prediction models.This study analyzes the Housing data of 5359 townhouses in Fairfax County, VA.The 10-fold cross-validation was applied to C4.5, RIPPER, Bayesian, and AdaBoost.RIPPER outperformed these other Housing price prediction models in all tests. House sales are determined based on the Standard & Poor's Case-Shiller home price indices and the Housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US Housing market. In addition to these Housing price indices, the development of a Housing price prediction model can greatly assist in the prediction of future Housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a Housing price prediction model. To improve the accuracy of Housing price prediction, this paper analyzes the Housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a Housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naive Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved Housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of Housing price prediction.

John M. Quigley - One of the best experts on this subject based on the ideXlab platform.

  • Index Revision, House Price Risk, and the Market for House Price Derivatives
    The Journal of Real Estate Finance and Economics, 2008
    Co-Authors: Yongheng Deng, John M. Quigley
    Abstract:

    It is widely recognized that options and futures markets for Housing can reduce and manage the risks inherent in consumers’ large investments in Housing equity. The integrity of such markets depends, however, upon the use of transparent and replicable benchmarks for house prices and settlement values. In the USA, a series of state and metropolitan indexes have been produced by a government agency (the US Office of Housing Enterprise Oversight, OFHEO), and they have been widely disseminated for over a decade. By construction, the entire historical path of each of these indexes is, in principle, subject to revision quarterly, that is, every time the index is recalculated and data are published. This paper provides the first analysis of the magnitude and bias of these revisions, and it analyzes their systematic effects on the settlement prices in Housing options markets. The paper considers the implications of these magnitudes for the development of risk-reducing futures markets.

Yongheng Deng - One of the best experts on this subject based on the ideXlab platform.

  • Index Revision, House Price Risk, and the Market for House Price Derivatives
    The Journal of Real Estate Finance and Economics, 2008
    Co-Authors: Yongheng Deng, John M. Quigley
    Abstract:

    It is widely recognized that options and futures markets for Housing can reduce and manage the risks inherent in consumers’ large investments in Housing equity. The integrity of such markets depends, however, upon the use of transparent and replicable benchmarks for house prices and settlement values. In the USA, a series of state and metropolitan indexes have been produced by a government agency (the US Office of Housing Enterprise Oversight, OFHEO), and they have been widely disseminated for over a decade. By construction, the entire historical path of each of these indexes is, in principle, subject to revision quarterly, that is, every time the index is recalculated and data are published. This paper provides the first analysis of the magnitude and bias of these revisions, and it analyzes their systematic effects on the settlement prices in Housing options markets. The paper considers the implications of these magnitudes for the development of risk-reducing futures markets.