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1 -7.2.1 HPI - House Pricing Index
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1 -5\. Metrics and KPIs.Metric and Key Performance Indicators.WebHome
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1 -= 7.2.1 HPI - House Pricing Index =
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5 -|=(% style="width: 79px;" %)Topic|=(% style="width: 139px;" %)Urban challenges|=(% style="width: 666px;" %)Sub-challenges
6 -|(% style="width:79px" %)Economy|(% style="width:139px" %)7 - Green Economy|(% style="width:666px" %)7.2 - Direct Economic Value of NBS
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10 -====== **Description** ======
11 -)))|(% style="width:730px" %)The indicator measures the price of residential building’s value in an area or group of areas. In this case, the value of housing act as a component of wealth in an area.
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13 -===== **Scale as linked to D2.2 framework** =====
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15 -* National
16 -* City
17 -* Neighborhood
18 -* Street
19 -* Household
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22 -====== **Data source [ link to data framework]** ======
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24 -* Information from the real estate market in the area of interest. This should include the characteristics of the houses.
25 -* Data in the following housing characteristics for a statistically relevant number of observations inside the area of interest:
26 -** The area of the structure of the house
27 -** The area of the land that the structure sits on
28 -** The location
29 -** The age of the structure
30 -** The type of structure (apartment, detached dwelling, etc.)
31 -** The materials used in the construction
32 -** The number of bedrooms, bathrooms, garage, and other facilities
33 -** Urban green areas characteristics: distance to houses, size, spatial configuration, species composition
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36 -====== **Needs** ======
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38 -* No specific tool is required, but statistical treatment of the information is necessary. Therefore, software such as excel or specific statistical software (R, SPSS, Past) will be required.
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41 -====== **Expected Outputs** ======
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43 -* Units: Euros/m2
44 -* Annual measurements
45 -* Quantitative
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48 -====== **Calculation methods** ======
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50 -* Extracted from Eurostat (2013) a calculation methods is proposed: hedonic regression method (time dummy variable method).
51 -** Definition of hedonic modelling (EUROSTAT, 2013, p.26). “Hedonic modelling assumes that information on the characteristics of the properties sold is known, the samples can be stratified and, if a sufficient number of observations is available, separate indices can be estimated for the strata. In other words, hedonic regression methods can provide a set of constant quality price indices for various types of property”
52 -** Calculation (EUROSTAT, 2013, p.50). In order to be able to estimate the marginal contributions of the characteristics using standard regression techniques, equation (5.1) has to be specified as a parametric model. The two best-known hedonic specifications are the fully linear model.
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56 -* In practice, many explanatory variables will be categorical rather than continuous and represented by a set of dummy variables which take the value of 1 if a property belongs to the category in question and the value of 0 otherwise.
57 -* Specificities of the Time Dummy Variable method to better account for changes on the quantities of the characteristics and their affection on the house price change can be found from page 51 of EUROSTAT (2013).
58 -* Also a case study showing how to perform the calculation is found in page 57 of the same report (with information of the case study on page 42).
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61 -====== **Links and references** ======
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63 -* EUROSTAT. 2013. Handbook on residential property indices. EUROSTAT, Belgium.
64 -* EUROSTAT. 2017. Housing Price Statistics. [Link]: http:~/~/ec.europa.eu/eurostat/web/housing-price-statistics [Last Access]: 01/10/2017
65 -* Cho, S.H., Poudyal, N.C. and Roberts, R.K. 2008. Spatial analysis of the amenity value of green open space. Ecological Economics, 66(2), pp.403-416.
66 -* Luttik, J., 2000. The value of trees, water and open space as reflected by house prices in the Netherlands. Landscape and urban planning, 48(3), pp.161-167.
67 -* Saphores, J.D. and Li, W. 2012. Estimating the value of urban green areas: A hedonic pricing analysis of the single family housing market in Los Angeles, CA. Landscape and Urban Planning, 104(3), pp.373-38
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