The thesis introduces several methods of real estate price modelling suitable either for prediction of the housing prices or for exploring the relationships between the price and its determinants. We compared the conventional linear regression approach to the tree-based methods of machine learning. The comparison analysis on the dataset of 28 019 apartments in Prague suggests that regression trees (especially the Random forest) yield a higher accuracy in the price prediction. Another objective was to examine the effects of location attributes (especially its accessibility and environmental quality) on the prices of nearby apartments. To address the spatial interactions in the geographical data, we employed three spatially conscious models to achieve more reliable results. The local analysis performed with the geographically weighted regression confirmed the presence of spatial heterogeneity and described the price effects relative to the location. In some areas, an increase of 100 meters in distance from the nearest metro station and the nearest park are associated with a decrease in the apartment prices by 644 CZK/m2 and 916 CZK/m2 , respectively. These findings are especially important for the apartments near the stations of the new metro line, which is currently in construction.

As housing is one of the important parts of gross domestic product and one of the most significant components of people's wealth it is vital to investigate the determinants of its prices. Therefore, we analyze housing prices in Central and Eastern European coun- tries using pooled mean group estimator and vector autoregressive models. The objective of this thesis is to find out whether the fundamentals of housing prices are comparable across different countries and how the shocks in the economy affect housing pricese. For our analysis we used housing prices per square metre, GDP per capita, unemployment rates, 5-year interest rates, harmonised indices of consumer prices and construction cost indices. The conclusions of using pooled mean group estimator suggest that GDP, un- employment, interest rate and HICP indeed significantly affect the housing prices. The results of empirical analysis of individual countries using vector autoregressive model con- clude that shocks in the determinants affect housing prices with lags of 2 to 3 quarters and that the individual countries are driven by slightly different fundamentals.

The empirical results from past research are quite clear. When the surrounding crime level goes up, housing prices go down. However, what has not been acknowledged in the previous literature is that different crimes might impact our willingness-to-pay heterogeneously. As most of the previous research is done through the usage of simple crime rates, this thesis acknowledges the relative severeness of different crimes. Using the newly developed crime harm index (CHI), the relative severity and harm inflicted by a specific crime is identified. The study is conducted in Sweden, Stockholm, using data for the year 2020. With the use of hedonic price equations, spatial models as well as graphical information system software, this thesis estimates a significant, and non-negligible negative relationship between increased area mean CHI and apartment prices. To the best of the author's knowledge, this is one of the very first analyses within the literature, which acknowledges the relative severeness of crimes, and the first to show a significant negative relationship between increased criminal harm and apartment prices. Further, the resulting estimates and method used in this thesis can be utilized to differentiate between the costs of different crimes, hence enabling precise cost estimates. These estimates could for example be of great use to policymaking, urban planners, and decision-making regarding safety investment.

This thesis examines the effectiveness of macroprudential policies on reducing housing price growth in the international database of 56 countries with the use of GMM and fixed effects between 2000 and 2017. The macroprudential index is added to the dynamic panel data model where the housing price index is regressed on housing price determinants as the economic growth or unemployment rate. The analysis is also conducted on the sample of countries with a higher market share of owners with a mortgage as there is a higher opportunity to control the housing market through the credit channel. Nevertheless, results show that we do not have enough evidence to state that macroprudential policies curb house price booms. Contrarily, the effect seems to work in the opposite direction which is probably caused by a reverse causality between the growth of real estate prices and the implementation of macroprudential tools. The debt-to-income restriction is the only tool that decreases housing price growth according to the fixed effects model. Detailed counterfactual analysis of the Czech market proposes only a slight impact of the loan-to-value measure on the apartment price development according to one out of four predictions. 1

There is no single commonly adapted model that explains the influence that various monetary policy instruments carry for the economy. During 2011-2017, the Swedish inflation rate has remained below the 2 percent target which has led the Riksbank to take measures aimed at stimulating the inflation. As of May 2018, the repo rate has experienced a number of decreases and is now at 􀀀0:50% which represents an unprecedentedly low level. With the inflation rate remaining below the target whilst the housing market has experienced substantial growth and recent decline, the question arises regarding what impact the repo rate exerts on various macroeconomic measures. In this paper, a statistical time series analysis is conducted using a Vector Autoregression model and the impulse responses are studied. A model of 7 economic variables is constructed to specially study the effect of the repo rate on employment and housing prices. Results demonstrate that rational expectations exist in the economy. Furthermore, results show that the repo rate influences factors affected by inflation rapidly, exerting maximum influence during the first year after the shock. On the other hand, real variables based on quantitative measures that are adjusted for inflation experience the greatest influence of the repo rate after a delay of 6 to 7 quarters. Employment experiences the greatest negative response to a repo rate shock after 7 quarters, with a magnitude of 0.317 standard deviations per standard deviation in the repo rate shock. Housing prices experience the greatest negative response to a repo rate shock after 4 quarters, with a magnitude of 0.209 standard deviations per standard deviation in the repo rate shock. / Det finns ingen allmänt vedertagen modell som beskriver olika penningpolitiska instruments påverkan på ekonomin. Under 2011-2017 har Sveriges inflationstakt legat under 2-procentsmålet vilket har fått Riksbanken att vidta åtgärder i syfte att stimulera inflationen. Fram till maj 2018 har upprepade sänkningar av reporäntan genomförts och den ligger i dagsläget på 0:50% vilket är den lägsta nivån någonsin. Då inflationstakten inte nått målet samtidigt som bostadsmarknaden har upplevt kraftig tillväxt och nylig nedgång uppstår frågan gällande vilken effekt som reporäntan utlovar på diverse makroekonomiska mått. I denna rapport genomförs en statistisk tidsserieanalys med en vektorautoregression och impuls-responserna studeras. En modell med 7 ekonomiska variabler skapas för att specifikt studera effekten av reporäntan på sysselsättning och bostadspriser. Resultaten visar att rationella förväntningar finns i ekonomin. Vidare visar resultaten att reporäntan influerar inflationspåverkade variabler omgående, med maximal påverkan inom det första året efter chocken. Å andra sidan påverkas volymbaserade variabler som justeras för inflation maximalt först efter en fördröjning på 6 till 7 kvartal. Sysselsättningen upplever störst negativ påverkan från en reporäntechock efter 7 kvartal motsvarande 0.317 standardavvikelser per standardavvikelse i chocken. Bostadspriser upplever störst negativ påverkan från en reporäntechock efter 4 kvartal motsvarande 0.209 standardavvikelser per standardavvikelse i chocken.

Läget anses vara den enskilt viktigaste faktorn som förklarar fastighetsvärden och bostadspriser, dock är begreppet väldigt brett. ​Inget läge är det andra likt och det är ofta det som gör bostäder speciella. Det är också detta som gör det problematiskt att värdera en bostad. Det råder en stor variation i bostadspriserna och hur de varierar är svårt att kartlägga på grund av mängden faktorer som spelar in.  Arbetet har som utgångspunkt att studera hur bostadspriserna beror av lägesvariabeln avstånd och hur det ser ut för småhus respektive lägenheter. Avståndsvariabeln innefattar avstånd till centrum samt avstånd till närmaste kollektivtrafik. Undersökningen kommer appliceras på en lokal nivå i Sundbybergs kommun och relaterar till en eventuell monocentrisk stadsmodell, då avstånd till stor del är temporalt och inte enbart geografiskt.  Den monocentriska stadsmodellen är en av de modeller som försöker förklara variationen utifrån bostadens läge. Enligt teorin bakom modellen ökar bostadspriserna desto närmre kärnan bostaden är belägen och på så sätt beror bostadspriserna av ett lägespris. Anledningen till varför priserna ökar närmre kärnan beror på att man är villig att betala mindre för transportkostnader till ett centrum dit stadens invånare antas transportera sig till dagligen för att nå arbete och önskad service. I denna rapport vill vi testa om den monocentriska stadsmodellen går att applicera på lokal nivå i Sundbybergs kommun genom att studera om det finns ett samband mellan bostadspriserna och avståndet till centrum.  För att kunna testa hur olika lägesfaktorer påverkar bostadspriserna används hedoniska prismodeller. Med en hedonisk prismodell kan man bryta ner ett observerat pris från sin helhet till implicita priser för varje attribut. Generellt är det en modell som kan tillämpas för att förstå bostadspriset. Arbetets syfte är att studera om det förekommer något samband mellan ett bostadspris och avståndet till ett lokalt centrum. Om bostadsprisstrukturerna kan kartläggas med modellerna skulle det kunna underlätta planeringen av nya områden och infrastruktur samt belysa de attribut som bör värdesättas högt i ett område. Syftet är också att undersöka hur bostadsprisstrukturen ser ut i ett område beroende på hur priserna influeras av avstånd till kollektivtrafik samt hur det skiljer sig mellan olika lokala områden. Enligt teorin från den monocentriska stadsmodellen bör bostadspriserna öka när avståndet till ett centrum minskar och med liknande resonemang borde bostadsprisena öka när avståndet till närmaste kollektivtrafik minskar.  För att uppnå syftet genomförs en litteraturstudie för att få bättre förståelse för teorin bakom den monocentriska stadsmodellen, den hedoniska prismodellen samt för att klargöra olika prispåverkande faktorer på bostadspriser. Utifrån teorin ställs hedoniska prismodeller upp där olika lägesvariabler analyseras.  Resultaten från de olika modellerna visar att för småhus är en monocentrisk stadsmodell applicerbar på Sundbybergs kommun. Däremot kan det inte dras någon slutsats om en monocentrisk stadsmodell för lägenheter. ​Principerna i den monocentriska modellen är svårapplicerade på området, främst för att Sundbyberg påverkas mycket av närheten till Stockholms innerstad men också för att kommunen är liten och inte homogen. Avståndet till närmaste kollektivtrafik visar sig ha motsatt effekt på bostadspriserna från hypotesen. Ägare till både småhus och lägenheter är inte villiga att betala mer för en bostad placerad nära kollektivtrafik. Däremot ​har närhet till kollektivtrafik en påverkan på slutpriset som överensstämmer med hypotesen i vissa områden i kommunen.  I rapporten är den insamlade datan begränsad vilket påverkar resultatet. ​För att ta fram en bättre värderingsmodell och kartläggning av faktorn avstånd måste därför fler lägesfaktorer samt andra prispåverkande faktorer inkluderas i modellen. / The location of a residence is considered to be the most important element when illuminate real estate values and housing prices, but the element is problematically due to its amplitude. The element, location, is unique and no housing is another comparable which implicates the difficulty to value a residence. There is a great variety in housing prices and how they varies is hard to identify due to the large amount of affecting elements.  The basis of this work is the study of how the housing prices depends on the location element and how it differs between houses and apartments. The location element implicate ​distance to a city center​ and ​distance to the closest public transport​ . The research will be applied at a local level in Sundbyberg and relate to a possible monocentric city model, where distance substantially is temporal and not only geografic.  One of the models that attempt to explain the location elements variety is the monocentric city model. According to the theory, housing prices increases the closer the object is situated to a city. The reason why the prices increases closer to the city is because citizens will be prepared to pay more for housing when the transportation cost decreases. This study examine inter alia if the monocentric city model can be applied on a local level in Sundbyberg by checking a potential relation between housing prices and the distance to city. To be able to try out the influence of various location elements on housing prices, hedonic price models are applied. A hedonic price model breaks down an ensembled observed price to implicit prices for each attribute. Generally, the model is carried out to understand the housing price. The intention of the work is to study if a relation between housing prices and the distance to a local city center anticipates. If the structure of housing prices can be charted with the models it would facilitate the planning of new domains and infrastructure, as well as illuminate attributes valuable in a region. This work also aim to examine how housing prices are influenced by distance to public transports and how that differs between various local areas. According to the monocentric theory, housing prices ought to increase when the distance to the city center decreases, with similar reasoning housing prices ought to increase when the distance to closest public transport decreases. To achieve the purpose a literature study is implemented to obtain immerse understanding of the theory behind the monocentric city model, the hedonic price model but also to clarify various elements that affect housing prices. Based on the theory, location elements are, in this report, analyzed in hedonic price models.  The result from the different models implicates that for houses in Sundbyberg a monocentric city model is applicable. However, no conclusion can be made for apartments in Sundbyberg. The principles of the model are hard to applicate on the area, mostly because Sundbyberg is affected by the vicinity to the inner city of Stockholm but also because of the small area and the lack of homogeneity. The distance to the closest public transport shows an opposite effect on the housing prices than the hypothesis. Both house and apartment owners are not prepared to pay more for a residence closer to a public transport. However, corresponding to the hypothesis, vincinity to public transport has in some local areas in Sundbyberg an impact on housing prices. The collected data in the report is limited which affect the result. To be able to receive a better valuation and clarification of the distance element, the model need to include more location elements and other elements that affect housing prices

by Chan Ngai-Ming, Zoe, Chung Ting Ting. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 56-57). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iii / LIST OF ILLUSTRATIONS --- p.v / LIST OF TABLES --- p.vi / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- THE HONG KONG PRIMARY RESIDENTIAL INDUSTRY --- p.6 / Industry Overview --- p.6 / Developers for This Study --- p.6 / Chapter III. --- THEORETICAL FRAMEWORK --- p.8 / Competitor Analysis --- p.8 / Market Commonality Vs Product Commonality --- p.8 / Suggested Attributes for Studying Product Commonality in this Industry --- p.9 / Location --- p.9 / Time of Sale --- p.9 / Flat Size --- p.10 / Other Factors --- p.10 / Resource Similarity --- p.12 / Competitive Actions and Responses --- p.12 / Product Commonality and Competitive Response --- p.14 / Competitive Response and Performance --- p.17 / Chapter IV. --- METHODOLOGY --- p.19 / Data --- p.19 / Identification of Actions and Responses --- p.19 / Measurement --- p.21 / Product Commonality --- p.21 / Likelihood of Response to Price Change --- p.24 / Response Magnitude --- p.24 / Performance --- p.25 / Comparative Approach --- p.25 / Chapter V. --- RESULTS --- p.27 / Product Commonality and Likelihood of Response to Price Change --- p.27 / Response Magnitude and Performance --- p.30 / Chapter VI. --- CONCLUSION AND DISCUSSION --- p.34 / Implications --- p.34 / Limitation and Future Directions --- p.36 / APPENDIX I RECOMMENDATIONS FROM STUDY OF HONG KONG RESIDENTIAL MARKET BY THE CONSUMER COUNCIL IN 1996 --- p.41 / APPENDIX IIA SUMMARY OF 27 DATA UNDER STUDIED --- p.43 / APPENDIX IIB TIMELINES FOR 27 DATA UNDER STUDIED --- p.46 / APPENDIX III PRODUCT COMMONALITY BREAKDOWN --- p.52 / APPENDIX IV SUMMARY OF PRIMARY DATA --- p.54 / bibliography --- p.56

Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.

The dissertation focuses on the problem of determining the timeliness of the normal market rent an apartment at the market price of the apartment. The basic premise is obtain a database of prices of flats and apartments to compare these parameters the most accurate way. This area has been addressed several times, but only in some cities and not fully explored. The conclusions of the dissertation indicate what the relationship is normal and usual rental price of the apartment, then further comparison of the development of this relationship before the economic crisis, ie the price levels of 2008 and the present. Based on these data, it is also possible to determine the level of capitalization in each regional cities with regard to the average wage in each regional cities.

PhD / Department of Development Studies