Assessing House Prices : Insights from “Houselev”, a Dataset of Price Level Estimates. Bricongne, J.; Turrini, A.; and Pontuch, P. Publications Office of the European Union.
Assessing House Prices : Insights from “Houselev”, a Dataset of Price Level Estimates. [link]Paper  abstract   bibtex   
House price assessments relying on price indexes only have a number of limitations, especially if the available time series are short and series averages cannot be taken as reliable benchmarks. To address this issue, the present paper computes house prices in levels for 40 countries: all the EU countries and a number of other advanced and emerging economies. The baseline methodology makes use of information on the total value of dwellings in national accounts statistics and on total floor areas of existing dwelling stocks from census statistics. This top-down methodology simply consists of estimating the average house price per square metre dividing the total value of dwellings for the total floor area. For some countries, the information to carry out the baseline method is not available. In such cases, price level estimates are based on property advertisements on realtors' websites. A correction factor is applied to address the upward bias of prices asked by sellers as compared with transaction prices and improve cross-country comparability. House price level estimates make it possible to compute price to income (PTI) ratios yielding a clear interpretation: the average number of annual incomes needed to buy dwellings with a floor area of 100 m2. Using a signalling approach aimed at identifying PTI threshold that maximises the signal power in predicting downward price adjustments, it is found that a PTI close to 10 works as an across-the board rule of thumb for identifying potentially overvalued house prices. Moreover, when price levels are used in regression-based models to estimate fundamentals-based house price benchmarks, they allow us to exploit the cross-section variation in the data thereby providing additional insights compared with analogous benchmarks based on indexes.
@book{bricongneAssessingHousePrices2019,
  title = {Assessing House Prices : Insights from “{{Houselev}}”, a Dataset of Price Level Estimates.},
  author = {Bricongne, Jean-Charles and Turrini, Alessandro and Pontuch, Peter},
  date = {2019},
  publisher = {{Publications Office of the European Union}},
  location = {{Luxembourg}},
  url = {https://doi.org/10.2765/807},
  urldate = {2019-10-16},
  abstract = {House price assessments relying on price indexes only have a number of limitations, especially if the available time series are short and series averages cannot be taken as reliable benchmarks. To address this issue, the present paper computes house prices in levels for 40 countries: all the EU countries and a number of other advanced and emerging economies. The baseline methodology makes use of information on the total value of dwellings in national accounts statistics and on total floor areas of existing dwelling stocks from census statistics. This top-down methodology simply consists of estimating the average house price per square metre dividing the total value of dwellings for the total floor area. For some countries, the information to carry out the baseline method is not available. In such cases, price level estimates are based on property advertisements on realtors' websites. A correction factor is applied to address the upward bias of prices asked by sellers as compared with transaction prices and improve cross-country comparability. House price level estimates make it possible to compute price to income (PTI) ratios yielding a clear interpretation: the average number of annual incomes needed to buy dwellings with a floor area of 100 m2. Using a signalling approach aimed at identifying PTI threshold that maximises the signal power in predicting downward price adjustments, it is found that a PTI close to 10 works as an across-the board rule of thumb for identifying potentially overvalued house prices. Moreover, when price levels are used in regression-based models to estimate fundamentals-based house price benchmarks, they allow us to exploit the cross-section variation in the data thereby providing additional insights compared with analogous benchmarks based on indexes.},
  isbn = {978-92-79-77438-6},
  keywords = {~INRMM-MiD:z-E2MZTQPW,costs,economic-value,economics,human-settlement,open-data,pricing,society,statistics,uncertainty},
  langid = {english},
  number = {101},
  series = {European {{Economy Discussion Papers}}}
}
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