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Söktermsdata som ledande indikator för bostadsmarknaden
KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management.
2015 (Swedish)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Search Queries As Leading Indicator Of The Housing Market (English)
Abstract [sv]

Den här studien utvärderar potentialen i söktermsdata från Google som ledande indikator för

priser på bostadsmarknaden i Stockholm. Det prediktiva innehållet i söktermsdata från

Google Trends jämförs mot en mer klassisk prognosmodell byggd på makroekonomiska

variabler. Genom att mäta avvikelsen i en pseudo-prognos redovisas respektive datakällas

förmåga till riktiga prognoser.

Den huvudsakliga slutsatsen är att det finns resultat som styrker tesen om prediktivt innehåll i

Googledata, framförallt för prognoser med horisonter upp till sex månader. Genom att

använda Googledata skapas prognoser som har en mindre avvikelse från den faktiska

tidsserien är vad modellen byggd på makroekonomiska variabler kan leverera. Resultatet visar

på användbarheten i söktermsdata från Google som ledande indikator för priser på

bostadsmarknaden i Stockholm.

Abstract [en]

This study evaluates the potential in using Google search term data as a leading indicator of

prices on the real estate market in Stockholm. The predictive content of the search term data

from Google Trends is contrasted against a more classic forecasting model using

macroeconomic variables. The ability of each data source to generate powerful forecasts is

demonstrated by measuring the deviation in a pseudo-forecast.

The main finding is that the results support the hypothesis on predictive content in Google

data, mainly forecasts with up to six months’ horizon. By using Google data, forecasts can be

made with less deviation from the actual time series than forecasts built on macroeconomic

variables. The results point to the usability of search term data from Google as a leading

indicator for prices on the real estate market in Stockholm.

Place, publisher, year, edition, pages
2015.
Keyword [en]
Google Trends, Forecast, Housing Prices
Keyword [sv]
Google Trends, Prognos, Bostadspriser
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-172117OAI: oai:DiVA.org:kth-172117DiVA: diva2:845790
Supervisors
Examiners
Available from: 2015-08-13 Created: 2015-08-13 Last updated: 2015-10-29Bibliographically approved

Open Access in DiVA

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CiteExportLink to record
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