http://landshape.org/enm/predicting-spatial-patterns-of-house-prices/ The Power of Numeracy Tue, 07 Feb 2012 17:57:00 +0000 hourly 1 http://wordpress.org/?v=3.3.1 http://landshape.org/enm/predicting-spatial-patterns-of-house-prices/#comment-5630 Фильмы Mon, 29 Jun 2009 22:50:06 +0000 http://landshape.org/enm/?p=12#comment-5630 Подскажите пожалуйста как тут зарегестрироваться чтобы оставить свою информацию с гиперссылкой на источник ? Статья будет о фильмах. Оплата 0.5$ Подскажите пожалуйста как тут зарегестрироваться чтобы оставить свою информацию с гиперссылкой на источник ?
Статья будет о фильмах. Оплата 0.5$

]]> http://landshape.org/enm/predicting-spatial-patterns-of-house-prices/#comment-5629 Simi Valley Wed, 01 Aug 2007 21:17:16 +0000 http://landshape.org/enm/?p=12#comment-5629 An excellent read, i've looked over the national association of realtors site as well and there are some fundamentaly valid points there as well. What throws it all into a tailspin is that markets are ultimately raised or dropped by the population. No math model can predict what event will turn a place into the "place to be" completely but this sure helps when that little part of the equation is left out. Good stuff! An excellent read, i’ve looked over the national association of realtors site as well and there are some fundamentaly valid points there as well. What throws it all into a tailspin is that markets are ultimately raised or dropped by the population. No math model can predict what event will turn a place into the “place to be” completely but this sure helps when that little part of the equation is left out. Good stuff!

]]> http://landshape.org/enm/predicting-spatial-patterns-of-house-prices/#comment-6035 Simi Valley Wed, 01 Aug 2007 21:17:00 +0000 http://landshape.org/enm/?p=12#comment-6035 An excellent read, i've looked over the national association of realtors site as well and there are some fundamentaly valid points there as well. What throws it all into a tailspin is that markets are ultimately raised or dropped by the population. No math model can predict what event will turn a place into the "place to be" completely but this sure helps when that little part of the equation is left out. Good stuff! An excellent read, i’ve looked over the national association of realtors site as well and there are some fundamentaly valid points there as well. What throws it all into a tailspin is that markets are ultimately raised or dropped by the population. No math model can predict what event will turn a place into the “place to be” completely but this sure helps when that little part of the equation is left out. Good stuff!

]]> http://landshape.org/enm/predicting-spatial-patterns-of-house-prices/#comment-5628 Surf » WhyWhere 2.0 server Wed, 26 Apr 2006 13:15:05 +0000 http://landshape.org/enm/?p=12#comment-5628 [...] Example: Predicting House Prces [...] [...] Example: Predicting House Prces [...]

]]> http://landshape.org/enm/predicting-spatial-patterns-of-house-prices/#comment-5627 ENM » Surprising finding #2 Wed, 19 Apr 2006 19:14:14 +0000 http://landshape.org/enm/?p=12#comment-5627 [...] Surprising finding #2 Filed under: Uncategorized — admin @ 7:14 pm By Landshape.org In an earlier post on the spatial analysis of increasing house prices in the US, I used the small set annual climate variables (7) and found that precipitation rather than temperature was a better predictor of metropolitan areas with increases greater than 20% in median price in 2005. Here I have run the analysis again using the new version of WhyWhere and the entire set of available terrestrial variables (All_Terrestrial). This time the best variable was etopo-terr (accuracy = 0.80), a raw elevation variable. The response graph shows the highest appreciation is in the category of lowest altitudes (tallest red column with frequency of background in blue). I think this is a more sensible results that achieved using climate variables, as appreciation has been well known to have been in coastal areas. The result is surprising as it illustrates that the WhyWhere approach generalizes to prediction of things other than biological species. An analysis on a non-biological set of data points will give a reasonable explanation when data-mining a large dataset of environmental variables. By comparison, use of the standard set of climate variables would give non-intuitive, if not spurious results. [...] [...] Surprising finding #2 Filed under: Uncategorized — admin @ 7:14 pm By Landshape.org In an earlier post on the spatial analysis of increasing house prices in the US, I used the small set annual climate variables (7) and found that precipitation rather than temperature was a better predictor of metropolitan areas with increases greater than 20% in median price in 2005. Here I have run the analysis again using the new version of WhyWhere and the entire set of available terrestrial variables (All_Terrestrial). This time the best variable was etopo-terr (accuracy = 0.80), a raw elevation variable. The response graph shows the highest appreciation is in the category of lowest altitudes (tallest red column with frequency of background in blue). I think this is a more sensible results that achieved using climate variables, as appreciation has been well known to have been in coastal areas. The result is surprising as it illustrates that the WhyWhere approach generalizes to prediction of things other than biological species. An analysis on a non-biological set of data points will give a reasonable explanation when data-mining a large dataset of environmental variables. By comparison, use of the standard set of climate variables would give non-intuitive, if not spurious results. [...]

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