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.

Objective: To evaluate changes in shopping behaviors among low-income families over a one-month period of time in Utah County, Utah. Design: Two researchers conducted thirteen 90-minute focus groups. Setting: Two community organizations serving low-income populations and a university campus. Participants: Seventy-two low-income adults who were the primary household food shoppers and who had at least one child less than 18 years in their household. Main Outcome Measures: Shopping behavior changes during one month period of time. Analysis: Focus groups were recorded and transcribed, and then coded independently by two researchers with any differences reconciled. Paired t-tests were used to test differences of food expenditures by food group between the beginning and end-of-the-month shopping behaviors. Results: Shopping habits among low-income families changed throughout the month and were impacted by use of food assistance programs, food prices, and shopping logistics. Participants reported purchasing more varied foods at the beginning of the month versus more starch-based and canned foods at the end-of-the-month. To overcome economic barriers, participants used numerous strategies including weekly or monthly menu planning, price matching, and bulk buying. Conclusions and Implications: Low-income families make strategic decisions based on economic circumstances and other factors, including participation in food assistance programs, or the timing of the month, in order to stretch food expenditures. Our results suggest limited economics throughout the month may hinder families' ability to consume a varied, nutrient-rich diet, which may impact future health status.

In this study, we investigate the relationship between national and global uncertainty with house prices. Uncertainty is measured with the economic policy uncertainty index developed by Baker et al. (2016). The relationship is evaluated with eight SVAR-models that are Cholesky decomposed to restrict the contemporaneous relationship between variables, this is used to model the housing market. We create two models for each country, one that includes the local uncertainty and one that includes global uncertainty. The studied countries are two larger and two smaller economies in the EU, namely Sweden, Denmark, Germany, and France. We investigate the impulse response functions to establish the short-run dynamics and then compare them amongst each other. The results show that uncertainty has a negative effect on house prices and that global uncertainty hasa larger impact than local uncertainty, except for Sweden´s case. Germany is most resilient to the effect of uncertainty among the studied countries. This can be because of the size of the rental housing market in Germany. Interestingly we also find that in all cases except for Denmark our models don’t find a consistent relationship between short-term interest and housing prices in the short run, which can be an indication of a bubble. Further studies are required to investigate how different housing policies affect the volatility of the housing market that is created by uncertainty.

In this study we analyse the relationship between renewable energy, electricity prices, and the stock market. The impact from electricity prices on stock markets have previously been thoroughly analysed. However, our study evaluates if a country’s share of renewable energy in their electricity production impacts the strength and size of the relationship in question. We use data from eight countries of rather equal economical sizes but that uses very opposed energy sources. Sweden, Norway, Finland, and Latvia represent countries with high amounts of renewable energy. While Belgium, Netherlands, Poland, and Hungary constitute countries with low shares of renewable energy. By using daily data between January 2016 and December 2021, we aim to understand the relationship of electricity prices and stock market indices and the role of renewable energy in this relationship. We do this by using Johansen’s cointegration test as well as analysing the correlation between volatilities through a DCC-GARCH(1,1). We find that both tests indicate a negative correlation between the electricity and stock markets as well as for their volatilities. In addition, we find some disparities between countries depending on their share of renewable energy. The impact of electricity prices on the stock market tends to be more pronounced for countries that use larger shares of renewable energy. Finally, findings suggest that the energy source used for electricity production also constitute an important factor in the connectivity of the markets. Wind power was found to be the main cause to the larger fluctuations on the electricity market leading to stronger relationship to the stock market. While hydro power is the more stable option of renewable energy with smaller variances and large storage capacity, weakening the link between the electricity market and stock market.

Financial intermediaries are paramount for modern society. During the last decade, however, our reliance on these institutions have been meticulously debated, especially in the aftermath of the financial crisis. This thesis contributes to this debate with a novel perspective on loan supply changes in light of the recent events in the Swedish real estate market. More specifically, it investigates what influence housing prices have on the supply of commercial and industrial loans. This is done by estimating dynamic panel data models using a quarterly panel containing balance sheet data for 68 Swedish monetary financial institutions, during the post-financial crisis period of 2009-2017. The results indicate that housing prices do not have a significant effect on commercial and industrial loan supply. However, these loans are to a considerable degree dependent on the institutes’ earlier levels of commercial and industrial loans.

Hushållens skulder har under det senaste decenniet ökat fortare än vad hushållens inkomster gjort vilket inneburit en stigande skuldkvot. En alltför hög skuldkvot innebär en stor risk för enskilda hushåll och banker men också för den makroekonomiska utvecklingen och den finansiella stabiliteten. Därför införde Finansinspektionen 1 mars 2018 ett skärpt amorteringskrav som innebär ett ökat krav på amortering vid nya bolån för hushåll med höga skuldkvoter.Syftet med detta arbete är att analysera det skärpta amorteringskravet och hur det kommer att påverka bostadsmarknaden i Stockholm. Detta har genomförts med intervjuer av representanter från olika sektorer på marknaden.De viktigaste frågorna som undersökts var vilka effekter av det skärpta amorteringskravet som kommer att vara tydligast, vilken demografi det kommer påverka och huruvida regleringen är en bra åtgärd. Det visar sig att amorteringskravet sannolikt kommer sänka skuldsättningen i landet, vilket är en önskad effekt från Finansinspektionens sida. Kritiker mot kravet menar att alternativa åtgärder är bättre med motiveringen att det går att nå samma effekt utan de negativa konsekvenser det skärpta amorteringskravet för med sig. De alternativa åtgärderna handlar om mer övergripande förändringar på bostadsmarknaden istället för en enskild reglering på bolånen. Det skärpta amorteringskravet verkar vara det bästa alternativet Finansinspektionen till förfogande för att stabilisera svensk ekonomi, men det finns andra åtgärder som hade varit ett bättre alternativ för att uppnå samma effekt. / In the last few decades there has been a faster increase in household debts than household income, which meant a rising debt ratio. An excessive debt ratio represents a high risk not only for individual households and banks, but also for macroeconomic developments and financial stability. Because of this Sweden implemented a tightened amortization requirement on housing loans for households with high debt ratios. The legislation was created by Finansinspektionen and implemented on March 1, 2018.The purpose of this work is to analyze the tightening of the amortization requirement and how it will affect the housing market in Stockholm. This has been done with interviews of representatives from different sectors on the market.The most important issues examined were which effects will be most prominent from the requirement, which demographics it will affect and whether the regulation is a good measure. It turns out that the amortization requirement is likely to lower debt in the country, which is the desired effect from Finansinspektionen. Critics against the legislation claims that alternative measures are better with the argument that the same effect can be achieved without the negative consequences of the increased amortization requirement. Alternative measures are more general changes in the housing market instead of a single legislation on mortgage loans. The new amortization requirement appears to be Finansinspektionens’ best available option to stabilize the Swedish debts. However, there are other measures that would have been better alternatives for achieving the same effect.

Syftet med denna rapport är att undersöka de medelvärdesåtervändande egenskaperna i aktiekursen för noterade fastighetsbolag med utgångspunkt för bolagets extraordinära substansvärde, samt hur väl information kan användas för investeringsbeslut.Resultatet av den empiriska utfallsstudien som utförts visar på att det finns ett samband mellan aktiekurs och substansvärden, samt att en investeringsstrategi baserat på medelvärdesåtervändande egenskaper i dessa variabler fungerar. Av de tre dynamiska portföljerna som tagits fram och som strävat efter att allokera störst vikt till de bolag som fundamentalt framstått som billigast jämfört med sitt genomsnittliga substansvärde, överträffar samtliga portföljer ett aktieindex av samma bolag. / This study investigates mean reversion in publicly traded property stocks with respect to the company’s extraordinary discounts to the net asset value; furthermore, to what extent this knowledge can be applied to investment strategies.The result of the empirical survey shows that there is a relationship between the stock prices and the net asset value, also, that an investment strategy utilizing mean reversion in these two variables works. Of the three dynamic portfolios, aiming to allocate the largest portfolio weight to the fundamentally cheapest stocks, based on their current discounts and premiums to the net asset value with respect to the company average, all three outperform an index of the same companies.

The Swedish energy system is changing and two major events that are taking place are the phase out of nuclear power and the increase of wind power. The associated changes affect the electricity market and the electricity producers, including combined heat and power plants. This thesis evaluates the Swedish energy system of 2025 with focus on electricity spot prices. It also investigates how a combined heat and power plant might perform in the future, given certain changes in the electricity price. Six different scenarios are developed where the electricity price is modified according to findings with regards to the influence of wind- and nuclear power. A model of a combined heat and power plant and a district heating network is created in BoFiT. The scenarios are applied to the model and results are analyzed in terms of heat production, choice of operational mode, merit order and economical performance. Major findings show a more volatile electricity price in 2025. Low price hours (<100SEK/MWh) occur throughout the year, while high price hours (>640SEK/MWh) take place mostly during winter - the season during which the heat demand is at its peak. Results show that the developed electricity prices require much more regulation from the modelled power plant and that the power plant is more adapted to handling high price hours than low price hours. The district heating network is also affected by the volatile electricity prices, and more frequent and greater variations are observed in the merit order. This suggests that in the future, the electricity prices will need to be followed more actively, and that a strategy will need to be developed, allowing for quick adaptation to the prices - communication and cooperation between the different actors in the network will be needed. / Sveriges energisystem är i förändring där avvecklingen av kärnkraft och ökad implementering av vindkraft är i fokus. Konsekvenserna av dessa förändringar kommer påverka elmarknaden och därmed elproducenterna, bland dem kraftvärmeverk. Detta examensarbete utvärderar energisystemet i Sverige 2025 med fokus på elmarknaden. Arbetet undersöker också hur ett kraftvärmeverk kan prestera i framtiden baserat på förändringar i elpriset. Sex olika scenarios har utvecklats där elpriset har modifierats baserat på analysen av vind- och kärnkraftsutvecklingen i Sverige och dess påverkan på elpriset. Ytterligare skapas en modell av ett kraftvärmeverk och ett fjärrvärmenät i BoFiT. Scenarierna implementeras i modellen och resultat extraheras och analyseras baserat på värmeproduktion, val av driftläge, körordning i systemet samt ekonomisk prestanda. Resultaten visar främst att volatiliteten i elpriset ökar till 2025. Låga elpristimmar (<100SEK/MWh) visar sig inträffa under hela året medan höga elpristimmar(>640 SEK/MWh) dominerar under vintern - säsongen där efterfrågan på värme är som högst. Resultaten visar att det förväntade elpriset kräver högre reglering av det modellerade kraftvärmeverket och att anläggningen idag är anpassad för att hantera framförallt höga elpriser men inte låga elpriser. Även fjärrvärmenätet i sig påverkas av volatilitet i elpriserna och mer frekventa och större variationer observeras i körordningen. Detta antyder att elpriserna i framtiden måste följas mer aktivt och att en strategi, som möjliggör snabb reglering för anpassning av elpriserna, måste utvecklas. Kommunikation och samarbete mellan parterna i fjärrvärmesystemet kommer därmed vara av hög betydelse.

Increasing urbanization in the US is leading to development or re-development of lands adjacent to solid waste facilities and these lands are being considered for residential communities and commercial projects. Thus, the potential for nuisance complaints against the pre-existing solid waste facility operations has become an increasing reality. The objective of this study was to develop a methodology to gather scientific and quantifiable data related to potential nuisances caused by landfills to determine setbacks and buffer zones near landfill and transfer station operations. Appropriate recommendations for these setbacks were made from case studies conducted at two landfills in Florida. The study involved making measurements related to odor, noise, litter and dust. Impact on housing prices was also evaluated by analyzing publicly available house price data. In this study volatile organic compound (VOC) concentration was used as a surrogate measure for gaseous impacts. The mass flux of VOCs was measured on the landfills using the dynamic flux chamber method. The ultimate purpose of flux measurements was to provide input data for dispersion modeling to analyze the extent of odor impact around the landfills, which is outside the scope of this study. Ambient measurements were also made around Landfill A for validating the dispersion model. Although there are no significant health and odor impacts caused by the landfill, higher background concentration extend 1.2-1.5 km from the landfill center on the Southeast side of the landfill. Litter from the road sides around the landfills was collected and catalogued based on size and material type. Litter count per site obtained for both landfills was less than the 2001 and 2002 state-wide counts. The difference was statistically significant. Noise measurements were made at landfills during incineration and landfilling. Based on average measurements (Leq) obtained at various distances from WTE facility and landfilling activity, and considering EPA recommended noise level of 55 dB(A) for a quiet neighborhood, a set back distance of 1.6-1.9 km was recommended. Impact on house prices near the landfills was done for four landfills in Florida. Analysis showed that three out of four landfills had significantly impacted the house price within 0.6-0.8 km from the edge of the landfill. Dust measurements were made at Landfill B using particulate samplers, quantifying the dust associated with landfilling. Measured values were below National Ambient Air quality Standard (NAAQ) for PM10. Finally, recommendations were developed to mitigate some of these nuisances