To reduce environmental impact from the real estate sector, improving energy performance and promoting the use of eco-labels are among essential elements. Understanding the relationship between the green initiatives, and their impact on property prices provides valuable insights into consumer preferences and market dynamics, from the perspective of asymmetric information. By applying a hedonic price regression on real estate prices in Malmö, the economic premium associated with energy efficiency and eco-labels are estimated over a five-year period to capture the development regarding green initiatives.  The aim of the thesis was twofold, to measure the economic premium of energy efficiency and eco-labels on residential housing in Malmö. The economic premium were found to be premium for energy efficient apartments, whereas the amount was found to decrease in relative terms between the examined years. The eco-label effect on apartments was found to be negative in 2015 and statistically insignificant in 2020. Single family houses showed no significant effect in the measured attributes.  The analysis of the data revealed that labelled housing is found to be clustered within highly populated areas, and not exclusively associated with higher income areas in the context of Malmö.

Rapporten undersöker hur bostadspriser påverkas av avståndet till kollektivtrafiken och sambandet det har till befolkningstätheten i området. Sedan så är det just också hur detta skiljer sig rent teoretiskt och i praktiken som skiljer det här arbetet från andra liknande arbeten då det oftast endast ett av detta och dess påverkan ligger i fokus. Insamlingen av data till arbetet har sket via den kvalitativ metod som avser att med hjälp av intervjuer, arkivdata och teorier som är anpassningsbara för att uppfylla syftet och besvara frågeställningen. Det finns några olika modeller och teorier som används som grund rent teoretiskt vid detta problem. Men stor skillnad mellan teorin och praktiken framkom snabbt då intervjuerna började då de flesta ej aktivt använde de modeller som kunde förklara avståndet till kollektivtrafikens påverkan samt befolkningstäthetens påverkan på bostadspriserna men i praktiken använde sig värderarna av marknadskännedom och ortsprismetoden vid nästan alla värderingar då de flesta ansågs att det räckte med dessa metoder för att värdera bostäder. Men så rent teoretiskt så finns det ett samband mellan avståndet till kollektivtrafiken och befolkningstäthet och det har oftast en positiv påverkan på bostadspriser men kan variera beroende på fler olika variabler som kan förekomma från fall till fall. Sedan så genom de fem intervjuer har empiriska data erhållits för att kunna koppla teorin till verkligheten. De fem respondenterna diskuterade först ifall sambandet fanns men även problematik med det, de flesta respondenter höll med att i praktiken finns det ett vist samband men att det var svårt att kvantifiera men att det ej är direkt och enbart mellan avståndet till kollektivtrafiken och befolkningstätheten då detta endast är två faktorer av väldigt många flera som påverkas tillsammans och förbättras oftast i liknande takt i följd av att det ställs högre krav och att ett område blir mer attraktivt att leva i. Slutligen så i praktiken behövs det i nuläget ej några nya modeller eller teorier för att kunna värdera bostäder med vikt på avståndet till kollektivtrafiken och befolkningstätheten, men att ifall dessa faktorer blir väldigt eftertraktade och attraktiva kan nya analyser och studier behövas göras för att marknaden ska kunna dra slutsatser om hur mycket dessa faktorer påverkar bostadspriserna. / The report examines how the housing prices get affected by the distance to public transport and the relationship it has with the population density in the area. Then it is precisely how this differs purely theoretically and in practice that separates this work from other similar work, since usually only one of this factors and its impact is in focus. The collection of data for the work has taken place via the qualitative method which intends to use interviews, archival data and theories that are adaptable to fulfil the purpose and answer the question. There are a few different models and theories that are used as a theoretical basis for this problem. But a big difference between theory and practice quickly emerged when the interviews began, as most did not actively use the models that could explain the impact the distance to public transport and the impact of population density on housing prices, but in practice the valuers used market knowledge and the local price method for almost all valuations as most considered that these methods were sufficient to value housing. But purely theoretically, there is a connection between the distance to public transport and population density, and this usually has a positive impact on housing prices but can vary depending on several different variables that can occur from case to case. Then through the five interviews, empirical data has been obtained to be able to connect the theory to reality. The five respondents first discussed whether the connection existed but also problems with it, most respondents agreed that in practice there is a certain connection but that it was difficult to quantify but that it is not directly and only between the distance to public transport and the population density as this are only two factors out of many that are affected together and usually improve at a similar rate as a result of higher demands being made and an area becoming more attractive to live in. Finally, in practice, there is currently no need for new models or theories to be able to value housing with weight on the distance to public transport and the population density, but that if these factors become very sought after and attractive, new analyses and studies may need to be done so that the market can draw conclusions about how much these factors affect house prices.

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This dissertation contains four essays in spatial and international economics. Chapter 1 investigates how housing variety varies across space. Housing costs are key in understanding real income differences across space and time. Standard measures of housing costs do not account for availability differences, where some housing varieties are available in certain cities or time periods but not others. When households have idiosyncratic preferences over housing units, the set of available housing varieties in a city matters. This paper develops theoretically-founded housing price indices to measure housing costs that account for availability differences. To allow for flexible substitution patterns, I propose a method to jointly estimate the nests that varieties belong to and the elasticity of substitution across varieties within each nest. I find that households in larger cities benefit from having access to varieties not available in smaller cities. Utility-consistent housing prices reduce the elasticity of housing prices with respect to population by a half. Since housing is a third of household expenditure, this implies that we have systematically underestimated real income and overestimated residual amenities in larger cities. In contrast to previous estimates, I find that real income is increasing in city size after accounting for availability differences. Chapter 2 investigates the factors that cause incomplete pass-through of exchange rate shocks into border prices. This paper examines the role of decreasing returns to scale, a channel that has received limited empirical and theoretical attention. Based on a first-order approximation to a firm's optimal price, I show that 1) decreasing returns to scale interacts with variable markups, imported inputs, and destination non-traded costs to generate incomplete pass-through, 2) there is asymmetry between importer currency and exporter currency shocks due to imported inputs, and 3) strategic complementarity matters, where firms adjust their prices in response to competitor prices. I propose a new estimation method for key demand and supply parameters that govern the degree of markup and marginal cost adjustments. Using the estimated parameters, I find that decreasing returns to scale is the dominant factor in generating incomplete pass-through, with variable marginal costs contributing to over 90% of the incomplete pass-through, while variable markups account for less than 10%. Chapter 3 analyzes the determinants of exporter size. Theories of comparative advantage and product differentiation have emphasized productivity and quality differences. This paper shows that incorporating decreasing returns to scale matters for understanding the determinants of exporter size. Exogenous marginal cost differences affect equilibrium quantities but do not necessarily appear in prices since lower exogenous marginal costs (a lower cost curve) are offset by higher endogenous marginal costs (movement along the cost curve). As a result, standard approaches that assume constant returns to scale underestimate the contribution of marginal cost differences and overestimate the contribution of quality differences. Based on bilateral trade flow data between 1997 to 2016 for over 200 countries and 3000 products, I find that standard approaches attribute almost no variation in exporter size to cost differences. In contrast, after incorporating decreasing returns to scale, I estimate that 58% (65%) of the variation in exporter size is attributed to fundamental cost differences in the time series (cross-section). Chapter 4 models and quantifies the dynamic gains from exporting. I develop a dynamic trade model where firms innovate and learn from other firms in the destinations they sell to. The evolution of a country's stock of knowledge can be expressed as a function of export flows and the stocks of knowledge of their trading partners. I find evidence that countries in Asia, North America, and Europe, as well as countries in the top two quartiles of TFP growth were able to better absorb foreign insights than other countries. I evaluate whether there are dynamic gains from trade with two counterfactual exercises. First, I measure the impact of changing trade costs between 1962 and 2000. I find small static gains but zero dynamic gains for the world economy. Second, I quantify the dynamic gains from export-induced foreign knowledge flows by simulating a counterfactual where there is no learning from foreign sources. I find that domestic learning compensates for foreign learning: there are large dynamic gains from exporting when there is no domestic learning and small dynamic gains when there is domestic learning.

Efter den relativt stora nedgången på börsen, som skedde i februari 2020 till följd av Covid-19pandemin, hade börsen en mycket stark uppgång mellan mars 2020 och november 2021. Närinflationen ökade under 2021 började fler prognostisera att det framgent skulle komma räntehöjningar.Detta, i kombination med andra faktorer, som exempelvis Rysslands invasion av Ukraina i början av2022, ledde till att många investerare blev passiva och skeptiska till hur aktiemarknaden skulleutvecklas. Från november 2021 till november 2022 föll det breda Stockholmsindexet, OMXStockholm PI, med 22 procent. Fastighetsindex OMX Stockholm Real Estate PI, hade en betydligtstörre nedgång på 44 procent. Syftet med studien var att undersöka vilka fastighetsbolag vars aktiekurser hade störst avvikelse frånfastighetsindex under tidsperioden november 2021 till november 2022, och varför. Studien harbaserats på både en kvalitativ och kvantitativ undersökning. I den kvalitativa undersökningen har 6mycket relevanta personer intervjuats. Den kvantitativa delen består av en sektorjämförelse samtdjupgående analyser av de 10 fastighetsbolag som avvek mest från index under den valda perioden. Resultatet visar att samtliga fastighetsbolag hade en negativ utveckling i dess aktiekurser mellannovember 2021 och november 2022. En orsak varför fastighetsbolagen påverkats mer negativt, jämförtmed andra sektorer, är att fastighetsbolag generellt använder högre nivåer av hävstång vilket blir merkostsamt i sämre finansiella klimat med högre räntor. Det finns många individuella faktorer somförklarar utvecklingen för varje specifikt bolag, dock har fyra faktorer konstaterats inneha en extra storbetydelse för hur aktiemarknaden har reagerat på de individuella bolagen. Detta har varitägarstrukturen, kapitalstrukturen, fastighetsportföljen och värderingen på bolaget vid ingången tillperioden. Det har varit viktigt att ha en stark finansiell position, både för bolaget men också dess ägare. Detta ärett mönster som har kunnat tydas via nyheter samt intervjuer. Gällande fastighetsportfölj haraktiemarknaden, bland de bolag som analyserats, handlat ner de fastighetsbolag som haft majoritetenav sin fastighetsportfölj bestående av bostäder och samhällsfastigheter. Bolagen som hade den störstanedgången i dess aktiekurser mellan november 2021 och november 2022 hade generellt högrevärderingar vid ingången till perioden. Detta berodde på att aktiemarknaden, vid de förändrademarknadsförutsättningarna, skiftade i vad som premierades. / After the relatively large drop in the stock market in February 2020 due to the COVID-19 pandemic,the stock market experienced a very strong recovery between March 2020 and November 2021. Asinflation increased in 2021, more people started to forecast future interest rate increases. This,combined with other factors, such as Russia's invasion of Ukraine in early 2022, led many investors tobecome passive and skeptical about the performance of the stock market. From November 2021 toNovember 2022, the broad Stockholm index, OMX Stockholm PI, fell by 22 percent. The real estateindex, OMX Stockholm Real Estate PI, had a much larger decline of 44 percent. The purpose of the study was to investigate which real estate companies whose share prices had thegreatest deviation from the real estate index during the period November 2021 to November 2022, andwhy. The study has been based on both a qualitative and quantitative survey. In the qualitativeresearch, 6 highly relevant persons have been interviewed. The quantitative part consists of a sectorcomparison and in-depth analysis of the 10 real estate companies that deviated the most from the indexduring the selected period. The result shows that all real estate companies had a negative development in their share pricesbetween November 2021 and November 2022. One reason why real estate companies have been morenegatively affected, compared to other sectors, is that real estate companies generally use higher levelsof leverage, which becomes more costly in poorer financial climates with higher interest rates. Whilethere are many individual factors that explain the performance of each specific company, four factorshave been found to be particularly important in determining how the stock market has reacted toindividual companies. These have been the ownership structure, the capital structure, the real estateportfolio and the valuation of the company at the beginning of the period. It has been important to have a strong financial position, both for the company and its owners. This isa pattern that has been evident from news reports and interviews. Regarding the real estate portfolio,among the companies analyzed, the stock market has traded down the real estate companies that hadthe majority of their real estate portfolio consisting of housing and community properties. Thecompanies that experienced the largest decline in their share prices between November 2021 andNovember 2022 generally had higher valuations at the beginning of the period. This was because thestock market, in the context of changing market conditions, shifted in what was rewarded.

This thesis investigates if house price prediction models perform better when adding macroe- conomic features to a data set with only house-specific features. Previous research has shown that tree-based models perform well when predicting house prices, especially the algorithms random forest and XGBoost. It is common to rely entirely on house-specific features when training these models. However, studies show that macroeconomic variables such as interest rate, inflation, and GDP affect house prices. Therefore it makes sense to include them in these models and study if they outperform the more traditional models with only house-specific features. The thesis also investigates which algorithm, out of random forest and XGBoost is better at predicting house prices. The results show that the mean absolute error is lower for the XGBoost and random forest models trained on data with macroeconomic features. Furthermore, XGBoost outperformed random forest regardless of the set of features. In Con- clusion, the suggestion is to include macroeconomic features and use the XGBoost algorithm when predicting house prices.