Det är viktigt i ett samhälle att ha institutioner som bidrar till ett effektivt utnyttjande av resurser. Syftet med denna artikel är att granska den svenska bostadsrättsformen utifrån de kriterier som Elinor Ostrom formulerat rörande vad som bidrar till en effektiv förvaltning av en ”allmänning”. Avslutningsvis presenteras också reformförslag som vi bedömer skulle minska problemen i dagens strukturer, problem som främst sammanhänger med att den som startar föreningen och bygger dess hus inte har något långsiktigt ansvar.

We consider an optimal investment problem in which the cost of the investment decreases over time. This decrease is modelled using the negative of a non-decreasing Levy process. The decreasing cost is a way of modelling that innovations drive down the cost of the investment. We present general results on how to compute both the value of the investment, as well as the optimal time at which the investment should be done. Several explicit examples of how different Levy processes influence the value of the investment are given as illustrations of the general results. The main tools used are fluctuation theory for Levy processes and inversion of Laplace transforms. When the inversion can be done analytically, we can present analytical solutions where in some cases only numerical solution has previously been known.

We consider an optimal investment problem in which the cost of the investment decreases over time. The decrease is modelled using the negative of a non-decreasing Lévy process. The decreasing cost is a way of modelling that innovations drive down the cost of the investment. Several explicit examples of how different Lévy processes influence the value of the investment are given. 

Many irreversible investment problems studied in finance has the property that the cash flow representing the cost and the revenue of the investment occur at one time (either at the same time, or at two different times). In this note we present a framework in which the cash flows are allowed to be spread out in time, thus yielding a more realistic model. We show the effect of this extension in an investment case study example. 

Traditional models of irreversible investment problems assume that the investment starts generating cash flows immediately, i.e., at the same time as the investment is undertaken. Real-world investment situations are characterized by time-to-build or investment lags, which means that there is a time difference between when the investment is made and when the investment starts generating cash flows. We combine two existing models of investment lags to obtain a flexible, yet simple, way of modelling and analyzing the effects of investment lags. Both traditional models, and models that incorporate the effects of time-to-build, typically assume that the expected future cash flows generated by an investment are represented by a single cash flow that reflects the size of the market value of an investment. To reflect real-world cases where investments generate cash flows in several time periods, we present a framework in which cash flows are explicitly allowed to be spread out in time. Our model can be used to incorporate cases where an investment is partially sold in different time periods. Using an irreversible optimal investment timing problem case study, we show how our framework makes it possible to easily compare the effect of different cash flow timings. In this case, the value and the timing of the investment depend on a constant that in a natural way can be decomposed into three parts, thereby showing the influence of the value and timing from the respective parts of the framework.

National house price indexes serve as a guideline and general indicator of the performance of real estate markets and industries. Many countries have experienced several periods of booms and busts in house prices during the last four decades. The periods of high house price volatilities are often highly interrelated with high uncertainties in not only housing markets but also in the credit markets and real economic activities. Thus, there is a need to understand and search for an appropriate method to measure and quantify the potential risk of sizeable losses in house prices.

There exist several Value at Risk (VaR) models that are used to measure asset price market risks. In this paper, we utilize quarterly panel real house price index series for 10 countries over the past four decades and apply different nonparametric, parametric, and semiparametric Value-at-risk models and methods to measure the downside risks of house prices. Our results highlight the importance of model choice regarding the calculation of VaR for the national house price indexes. The main contribution of this paper is the evaluation of the forecasting capability of nonparametric, parametric, and semiparametric risk models applied to national real house price indexes and showing that one specific model is not optimal for all the national indices: one suit doesn't fit all.

Bakgrunden till denna rapport är de höga priserna och höga hyrorna i nyproducerade bostäder. Debatten fokuserar ofta på markpriser och de direkta byggkostnaderna, men i denna rapport behandlas (delar av) det som kallas byggherrekostnader.

Begreppet har ingen enhetlig definition och i denna rapport fokuseras på kommunala planoch bygglovsavgifter som anslutningsavgifter för vatten och avlopp samt el. I byggherrekostnader kan man även räkna in olika skatter och kostnaderna för det egna arbete som byggherren lägger ner, men de behandlas inte här.

Data har samlats in från kommuner geografiskt spridda över landet och med olika storlek, rörande avgifterna år 2015 och 2020. Det rör avgifter för styckebyggda småhus i en viss antagen storlek, densamma vid båda tidpunkterna (tomtarea 800 kvm och boyta 160 kvm). Därutöver har kompletterande intervjuer med kommunala tjänstemän och en enklare litteraturstudie genomförts i försök att besvara vad som kan förklara byggherrekostnadernas utveckling.

Att jämföra prisutvecklingen med Konsumentprisindex (KPI) kan vara orättvist eftersom fallande importpriser påverkat den senare. Tjänsteprisindex (TPI) bedömdes därför som lämpligare och TPI steg under den aktuella perioden med 7%.

I de studerade kommunerna har plan- och bygglovsavgifter ökat med 10% under perioden, från i genomsnitt ca 110 000 kr till ca 120 000 kr. VA-anslutningsavgifter har ökat med 20%, från drygt 140 000 kr till drygt 170 000 kr. Elanslutningsavgifterna har utvecklats på liknande sätt, från knappt 25 000 kr till drygt 35 000 kr. Rapporten visar att byggherrekostnadernas utveckling har varit kraftigt negativ i vissa utvalda kommuner, och på motsatt sätt, kraftigt positiv i andra kommuner. Ett exempel är Sundbybergs kommun, vars plan- och bygglovsavgifter har minskat med 31%. Samtidigt har t.ex. Haninge kommun ökat motsvarande avgifter med 40%. Dessa procentsatser indikerar att avgifterna påverkas av förd politik i den aktuella kommunen.

Rapporten visar att det finns flera möjliga rationella skäl bakom ökningen, men att det samtidigt inte går att försumma att ökningarna i kommunernas avgifter skulle kunna bero på ineffektivitet och missbruk av dominerande ställning. Det är tydligt att utformningen av kommunala taxor är komplex till sin natur, där en mängd faktorer behöver tas i beaktning för att motivera taxans storlek. Arbetet med de kommunala taxorna har visat sig vara en kontinuerligt pågående process. Till syvende och sist så ska dock taxans storlek utformas utifrån hur kostnaderna ser ut för kommunen. Det är bevisligen så att flera kommuner uppfattar prissättningen som komplex. Aktörer som SKR bär därför ett stort ansvar för att kunna bistå kommuner i att räkna ut rätt taxa. Av denna anledning så påpekas det i rapporten att själva prissättningsprocessen bör utredas närmare.

Därutöver föreslås det i rapporten att SKR, t.ex. genom webbtjänsten Kolada, bör samla och presentera kommunala PBL-taxor över tid. En av anledningarna till varför SKR bör göra detta är de bevisliga svårigheterna i att hämta information om PBL-taxor från kommuner över tid.

This licentiate thesis is a collection of two essays that utilize time-series econometric methods in real estate finance. The first essay applies econometric modelling on Real Estate Investment Trust (REIT) index returns, focusing on estimating the effect of the quantitative easing (QE) and quantitative tightening (QT) programmes on U.S. equity REIT index returns, while controlling for several other important macro-financial factors. The second essay instead focuses on forecasting U.S. equity REIT index returns empirically, where the performance of a traditional econometric model (ARIMA) is compared to a modern state-of-the-art deep learning-based model (LSTM).

Digitalization, which encompasses a broad range of technological advancements, is the main factor that we study for its impact on REIT investments. One perspective on the impact of digitalization on REITs is its effect on inflation. Digitalization has the potential to increase productivity and reduce costs, which could help to keep inflation low. This, in turn, has in the recent decades provided a supportive environment for REIT investments through lower interest rates, which we partly investigate in the first essay.

Another perspective is that digitalization has not only led, but is also expected to lead, to significant innovations in the field of artificial intelligence (AI) and machine learning (ML), including deep learning (DL), which is a subset of ML. Many researchers and market practitioners are currently working to develop models that can use large amounts of data to make better forecasts and investment decisions. If successful, these models could significantly improve the performance of REIT investments. Can DL models be trained to make better forecasts for making investments? This is a question we ask ourselves in the second essay.

The study of digitalization and its effects on inflation has been a growing area of interest in recent years, with researchers exploring the potential impact of technological advancements on macroeconomic trends, which founded the base to our studies. However, recent developments in the global economy have shifted the focus of this research, as inflation levels have unexpectedly risen from what was previously believed to be a low and stable environment. As a result, the setting and framework for our research on digitalization and inflation have been significantly altered, as we have tried to adapt to this changing landscape.

The Federal Reserve (the Fed) has implemented several quantitative easing (QE) programmes to stimulate the U.S. economy and increase the inflation rate after the great financial crisis (GFC) and the COVID-19 crisis. However, when the inflation rate started to increase steeply in 2021, the Fed instead begun to implement quantitative tapering (QT) to cool down the U.S. economy and bring back inflation to it target rate. This study seeks to estimate the effect of the QE and QT programmes on the U.S. equity Real Estate Investment Trusts (REITs) index returns, while controlling for several other important macro-financial factors. The estimations show that the QE programmes significantly contributed to a long period of positive REIT returns, while the recent 2022 QT efforts has contributed significantly to the recent period of negative REIT returns. We also find that the increases in the key macro-financial factors Baa Corporate Bond Yield ad the CBOE volatility index of the U.S. stock market (VIX) result in lower REIT returns, while increases in total bank equity capital of FDIC-Insured Commercial Banks and Savings Institutions contribute to positive REIT returns. We also find that the negative initial REIT return reaction to the COVID-19 outbreak was likely outperformed by the positive impacts of the large combined monetary (QE) and fiscal stimulus packages implemented after the outbreak of the COVID-19 crisis.  The findings of this study show that REIT returns are highly sensitive to profound QE and QT programmes through important monetary transmission mechanisms channels such as the interest rate, asset price and risk-taking channels. This research supports REIT investors to understand how the Fed's monetary policy actions, particularly QE and QT programmes, impact the returns of the REIT index, and to adjust their investment strategies accordingly based on their expectations of future monetary policy actions and macro-financial conditions.

This study aims to investigate whether the newly developed deep learning-based algorithms, specifically Long-Short Term Memory (LSTM), outperform traditional algorithms in forecasting Real Estate Investment Trust (REIT) returns. The empirical analysis conducted in this research compares the forecasting performance of LSTM and Autoregressive Integrated Moving Average (ARIMA) models using out-of-sample data. The results demonstrate that in general, the LSTM model does not exhibit superior performance over the ARIMA model for forecasting REIT returns. While the LSTM model showed some improvement over the ARIMA model for shorter forecast horizons, it did not demonstrate a significant advantage in the majority of forecast scenarios, including both recursive multi-step forecasts and rolling forecasts. The comparative evaluation reveals that neither the LSTM nor ARIMA model demonstrated satisfactory performance in predicting REIT returns out-of-sample for longer forecast horizons. This outcome aligns with the efficient market hypothesis, suggesting that REIT returns may exhibit a random walk behavior. While this observation does not exclude other potential factors contributing to the models' performance, it supports the notion of the presence of market efficiency in the REIT sector. The error rates obtained by both models were comparable, indicating the absence of a significant advantage for LSTM over ARIMA, as well as the challenges in accurately predicting REIT returns using these approaches. These findings emphasize the need for careful consideration when employing advanced deep learning techniques, such as LSTM, in the context of REIT return forecasting and financial time series. While LSTM has shown promise in various domains, its performance in the context of financial time series forecasting, particularly with a univariate regression approach using daily data, may be influenced by multiple factors. Potential reasons for the observed limitations of our LSTM model, within this specific framework, include the presence of significant noise in the daily data and the suitability of the LSTM model for financial time series compared to other problem domains. However, it is important to acknowledge that there could be additional factors that impact the performance of LSTM models in financial time series forecasting, warranting further investigation and exploration. This research contributes to the understanding of the applicability of deep learning algorithms in the context of REIT return forecasting and encourages further exploration of alternative methodologies for improved forecasting accuracy in this domain. 

This thesis study includes quantitative and qualitative research on real estate tokens, one of the leading security tokens. Security tokens, which are based on blockchain technology, are rapidly becoming widespread as new era investment products. Real estate tokens have long stood out as one of the most popular of these tokens. The underlying reason is that the real estate industry is associated with low liquidity and long and expensive transaction processes. Tokenization platforms and real estate market experts believe that tokenization will solve many problems in the traditional market. The products that will emerge through the tokenization of real estate assets can increase liquidity by removing high entry barriers in the market and create a secondary market where intermediaries are minimized. Theoretically, this technology, which can provide secure access to a wide market in a short time, can also mean a new platform for both debt and capital increase. Within the scope of this study, the structure of real estate tokens as financial products was examined, their similarities and differences with traditional products were discussed. Moreover, an empirical analysis has been made by comparing the financial performance of real estate tokens that are actively traded in the secondary market with certain reference indices. The empirical result found that token indices performed favorably with and relative to the crypto market and traditional market indices in the context of risk-adjusted returns.

The issue of housing supply in Sweden is constantly debated, and there are differing opinions on whether there is a housing shortage for the growing population. The increased average age in Sweden leads to a greater need for accessible and adapted housing for the elderly. The majority of the elderly choose to remain in their homes for as long as possible, while the municipality, with its responsibility for housing provision, works to offer alternative housing options for the elderly. This thesis examines how elderly individuals currently live and what housing preferences they have for the future. The purpose of this research is to increase understanding of the housing supply for the elderly in Sweden and to shed light on its effectiveness and the future need for housing for the elderly population. The study is based on the municipalities' own work, assessments, and strategies. The research examined the housing market for the elderly in eight Swedish municipalities. The study found that there is a demand for housing that is not needs-assessed accommodation, but the supply does not meet this demand. A housing format that acts as an intermediate housing between ordinary housing and special housing is what the sample municipalities consider is lacking. The results also revealed a lack of fixed guidelines for housing supply planning for the elderly in accordance with the municipalities' responsibilities. The discussion also addressed issues of location problems and the adaptation of existing need-tested accommodations to meet needs. In conclusion, the study highlights a collision between housing needs and the supply of housing for the elderly, as well as the need for clearer planning and strategies to meet these needs.

Different industries exhibit significantly different leverage; companies in the real estate investment trust (REIT) and technology/hardware sectors are extreme examples. In the United States, the leverage ratio is twice as high for REITs (50%) as compared to non-real-estate firms (around 25%), and the technology/hardware sector has the lowest ratio (around 17%). We theoretically and empirically analyze their differences. By decomposing the difference into three channels, we find that the industry-specific channel explains around 67% for REITs and 68% for technology/hardware firms; the value-based channel is mostly responsible for the remaining portion. Taking the nonlinear influences of extreme values into account, the relevance of the industry-specific channel is considerably reduced.

Different industries exhibit significantly different leverage – companies in the REIT and technology/hardware sectors are extreme examples. The leverage ratio is twice as high with 50% for REITs as for non-real estate firms with around 25% in the U.S.; whereas the technology/hardware sector has the lowest ratio with around 17%. We theoretically and em­pirically analyse their differences. By decomposing the difference into three channels, we find that the industry-specific channel explains around 67% for REITs and 68% for technology/hard­ware firms; the value-based channel is mostly responsible for the remaining part. When taking non-linear influences of extreme values into account, the relevance of the industry-specific channel is considerably reduced.

We present and expand existing theories about why individuals may assess positive outcomes differently from negative outcomes in intertemporal choices. All of our theories – based on utility or cost considerations – predict a conventional magnitude effect for positive outcomes, i.e., a negative relation between outcome size and subjective discount rates. For negative outcomes, however, implications are different for utility- and cost-based approaches. We argue that the relevance of utility-based aspects is strengthened in a money frame, leading to a conventional magnitude effect even for negative outcomes, whereas cost-based considera­tions gain in importance in an interest rate frame, implying, in contrast, a “reverse” magnitude effect, i.e. higher discount rates for (absolutely) higher outcome size. By conducting a web-based experiment with 676 participants, we confirm our theoretical findings and conclude: the conventional magnitude effect prevails for positive outcomes in the money and the interest rate frame and for negative outcomes in the money frame. However, there is a reverse magnitude effect for negative outcomes in the interest rate frame. Our results might help to better understand prevailing magnitude effects in practical applications and might also be apt to derive suggestions for better designing of intertemporal decision problems.

We present and expand existing theories about why individuals may assess positive outcomes differently from negative outcomes in intertemporal choices. All of our theories—based on utility or cost considerations – predict a conventional magnitude effect for positive outcomes, that is, a negative relation between outcome size and subjective discount rates. For negative outcomes, however, implications are different for utility- and cost-based approaches. We argue that the relevance of utility-based aspects is strengthened in a money frame, leading to a conventional magnitude effect even for negative outcomes, whereas cost-based considerations gain in importance in an interest rate frame, implying, in contrast, a “reverse” magnitude effect, that is, higher discount rates for (absolutely) higher outcome size. A web-based experiment with 676 participants confirms our theoretical findings: the conventional magnitude effect prevails for positive outcomes in the money and the interest rate frame and negative outcomes in the money frame. However, there is a reverse magnitude effect for negative outcomes in the interest rate frame. Our results might help to better understand prevailing magnitude effects in practical applications and might also be apt to derive suggestions for better designing of intertemporal decision problems.

For varying borrowing and lending amounts, the corresponding subjective discount rates will also vary. A situation where high amounts correspond to lower discount rates is called a conventional magnitude effect, while the opposite is called a reverse magnitude effect. We present an overview of the theoretical arguments for both kinds of magnitude effects. Against this background, we then offer the first comprehensive empirical analysis of this issue based on real-life transaction data. To do so, we rely on more than 9,000 credit applications from the formerly largest German peer-to-peer (P2P) lending platform, Smava, between February 2007 and April 2013. We confirm that there is a conventional magnitude effect for lending money to others but a reverse magnitude effect for borrowing decisions. We suggest, as an explanation for our findings, the prevalence of cost-based determinants of magnitude effects in this special setting.

This paper is organized as follows. In Section 1, we describe the economic relevance of the real estate sector and its recent dynamics. Then, we identify the most mentioned keywords of working papers presented at the real estate conferences between 2015 and 2019 and showing network figures for them in Section 2. In order to identify the newest trends, we rely on working papers since they have an average lead time of at least 1 to 2 years before they are published. In addition, we give a short overview of the articles published in this special issue. To get a better overview of the relevance of real estate related topics in finance, we analyzed the most relevant finance conferences and journals between 2015 and May 2020 in Section 3. To find the topics, we apply the text mining approach Latent Dirichlet Allocation (LDA), an unsupervised machine learning method. The real estate trends (retail, e-commerce) and the potential impact of COVID-19 is described in Section 4.

Purpose This paper aims to analyze how nearby property prices are affected by new construction projects in Stockholm. If there is an impact on property prices, the authors endeavor to investigate whether the effects vary among different areas within the municipality, for different groups of inhabitants and for different types of housing (i.e. public versus private housing). Design/methodology/approach The authors use a difference-in-difference specification in a hedonic model, and the sample consists of more than 90,000 observations over the period 2005-2013. Findings The results are robust and indicate that house prices in nearby areas increase following the completion of infill development. The results also indicate that infill development has a positive spillover effect on nearby dwelling prices only in areas with lower incomes, more public housing units and more inhabitants born abroad. Originality/value It provides an analysis on how nearby property prices are affected by new construction projects by creating a restricted control area, so as to make the treatment group and the control group more homogeneous. Thus, it mitigates any potential problems with spatial dependency, which can cause biased standard errors.

A combination of strong urbanization and shortage of land in many European city areas prompts an impetus of infill development, with current residents often raising concerns that infill development leads to lower nearby property prices. The aim of this paper is to analyze how nearby property prices are affected by new construction projects in Stockholm, Sweden. We use a difference-in-difference specification in a hedonic model, and our sample consists of more than 40,000 observations over the period 2005–2013. Our results are robust and indicate that house prices in nearby areas increase following the completion of infill development. Our results also indicate that infill development only has a positive spillover effect on nearby house prices only in areas with lower incomes, more public housing units and more inhabitants born abroad.

This paper investigates gender roles in children’s savings, specifically defined as differences pertaining to age, capital invested, financial activity and portfolio composition, using a sample of 58,000 children. We observe gender differences between young female and male account holders. The average age and activity level of boys are significantly higher whereas girls hold more capital in their accounts. We note that activity interacts with both gender, age and capital, and is decisive in explaining financial behaviour, especially that of boys. We conclude that girls have a lower share of saving in direct-owned stock already before the age of one. This difference lingers throughout childhood and peaks for children aged 15-17. We discuss our findings applying structuration theory, differentiating between implicit and explicit parental impact prevailing among adolescents, and the definite caring regarding younger children. For policymakers and managers, awareness that gender differences regarding financial behaviour prevail is an important insight.

In this study, we analyse how, and to what extent, direct and indirect effects (spatial spillovers) matter when estimating price effects for a property location in a flood zone. Unlike the previous literature, we show the importance of indirect effects resulting from a neighbourhood being situated in a flood zone. Additionally, the type of indirect effects (global vs. local) needs to be determined theoretically or empirically using an appropriate spatial model comparison approach. Using the Bayesian model comparison for data related to the flood-prone city of Dresden, Germany, we find strong evidence for a spatial Durbin error model which controls for local spillover effects. These indirect price effects amount to -6.5% for houses and -4.8% for condominiums. However, direct effects diminish when controlling for spatial spillovers. Our results are mostly robust across different model specifications, urban areas, and risk-adjusted prices that include future insurance costs, thus providing evidence of the importance of addressing indirect effects in the form of local spillovers in the analysis of flood zone effects. Ignoring indirect flood effects when formulating policy can lead to flood management that is inefficient and not cost-effective, as the economic consequences of flood are underestimated.

This study makes use of a dataset recently collected among young people in Stockholm, Sweden (N = 1122), to investigate the impact of fear on young people?s mobility and precautionary behaviour, after controlling for previous victimization as well as situational characteristics of daily trips. Geographical Information Systems (GIS) and logistic regression models underlie the methodology of the study. Previous victimization, especially for sexual crimes, triggers precautionary behaviour among young people using trains and the metro. Signs of poorly maintained transit environments also affect young riders? mobility patterns, as they state that they avoid particular stations or routes at particular times. Informed by principles of environmental criminology and the theory of fear of crime, the implications of the findings for both theory and practice are discussed.

Safety and security are human rights - to feel free from risk and fear of danger is crucial for all people and a prerequisite for modern society. Safety promotes and encourages mobility, which is basic for an individual’s quality of life. This report presents Stockholm students' responses to a survey of experiences and perceptions of sexual harassment and sexual transit crimes in public transport conducted in May-June 2018 as part of an international study. The study contained 1,122 young people surveyed at the Royal Institute of Technology (KTH) in Stockholm and 309 students from Södertörn University in Huddinge. We investigated the victimization of sexual harassment and sexual crimes in transit environments, feelings of perceived security or lack thereof, and necessary improvements to make travel safer.

Rent control measures are typically in place to assist low-income households and decrease segregation. Yet, there is little empirical research on the social impact of such policies and specifically the role of how rent-controlled apartments are allocated. This study analyzes the income-distributional effects of rent control with a novel dataset that includes characteristics of those who received rent-controlled apartments between 2011 and 2016 in central Stockholm, Sweden. Specifically, this paper provides analysis of the impact of allocating apartments through a centrally managed queue with apartments from both public and private landlords. To quantify the rent subsidy, we estimate hypothetical market rents by taking the owner-occupied market as a point of deviation. We find a positive relationship between the rent subsidy and time in que. Apartments in the fourth quartile of subsidy require on average 21 years in que, while those in the first quartile require 10 years on average. There is considerably heterogeneity in the level of rent subsidy, and tenant income. Even as allocating through queuing should benefit high-income households less than allocation based on landlord preferences, we find several regressive effects. Controlling for time in queue, we find that tenants in the fourth quartile of annual income receive monthly rent subsidies that are substantially higher than renters in the first quartile of income. Similarly, rental apartments in the fourth quartile of the subsidy have older tenants with substantially higher incomes compared to less subsidized apartments. The regressive effect is driven by high earners renting larger apartments with larger absolute subsidies and being able to wait longer in queue.

Despite the extensive international interest in house price dynamics and the growing body of research in this area, until now it was not that evident what the fundamental factors are that determine house prices and what the size of their potential impact is on house price dynamics over the long run.

This study provides a systematic literature review of empirical studies on the fundamental determinants of house prices in advanced economies at the aggregated level. It is based on analysis of journal articles published in 58 top-tier housing, urban and real estate journals using bibliometric analysis. The synthesis on house price determinants involves 66 selected studies published in English during the last five decades (1973-2022) and represents a summary of the developments in the field of housing economics related to house price dynamics.

The results of the study suggest that most of the research on house price determinants employed quantitative methodology with a wide variety of econometric techniques. The study presents several key findings related to house price dynamics over the long run. Particularly, it defines fundamental determinants of house prices, classifies them in relation to the size of their elasticities and emphasizes the differences between them. In addition, the study describes the role of data sources as well as quality and modelling techniques, and discusses the impact of inflation, momentum, mean reversion and housing market efficiency. The study ends by assessing the outlook for potential extensions of the existing literature and outlines further research areas.

The dynamics of housing markets constitute a complex phenomenon. The body of research is vast and includes thousands of studies done on the national and international level. Empirical evidence indicates that housing markets differ in local institutional arrangements and that these might affect the ways they develop over time. To analyze housing market dynamics in various institutional environments and to increase our understanding of them is the main objective of this thesis.

The scope of research includes analysis of relevant theories and empirical models that might explain development of house prices over the long run. Given that prices affect housing construction, this thesis also aims to deepen understanding of the impact of the relationship between new construction and underlying fundamentals, together with various institutional arrangements that might differ between countries. In particular, they include bank lending policies, valuation methods for mortgage purposes, different regulatory measures like rent control, as well as land and building policies.

The Swedish apartment market was chosen for empirical analysis. The analysis is carried out by a combination of qualitative and quantitative methodology and applies several research methods, such as systematic literature review, and panel data analysis with application of advanced econometric techniques like Dynamic Ordinary Least Squares, Fixed and Random Effects and Seemingly Unrelated Regressions. Four studies are carried out using these methodologies.

The results indicate that the interest rate, disposable income and population growth seem to be major determinants of house prices according to studies found in the empirical literature (Paper 1). The study also classifies house price determinants in relation to the size of their elasticities. However, fundamental factors cannot always provide an explanation for the deviations in house price dynamics in different countries, whereas the institutional environment might do so (Paper 2). The analysis demonstrates that land prices and building policies, along with a number of market fundamentals, affect the average size of an apartment in new residential construction (Paper 3). In the presence of rent control, the rent or price level does not contribute by adding new units to the total housing stock. In addition, the displacement effect occurs primarily in the market’s rental sector and not in the tenant-owned apartments segment (Paper 4).

This research contributes to a better understanding of housing markets dynamics and suggests policy implications that might reduce the risks for housing bubbles and improve the socio-economic development of the society over the long run.

Purpose: This study examines the long term effects of macroeconomic fundamentals on apartment price dynamics in major metropolitan areas in Sweden and Germany. Design/methodology/approach: The main approach is panel cointegration analysis that allows to overcome certain data restrictions such as spatial heterogeneity, cross-sectional dependence, and non-stationary, but cointegrated data. The Swedish dataset includes three cities over a period of 23 years, while the German dataset includes seven cities for 29 years. Analysis of apartment price dynamics include population, disposable income, mortgage interest rate, and apartment stock as underlying macroeconomic variables in the model. Findings: The empirical results indicate that apartment prices react more strongly on changes in fundamental factors in major Swedish cities than in German ones despite quite similar development of these macroeconomic variables in the long run in both countries. On one hand, overreactions in apartment price dynamics might be considered as the evidence of the price bubble building in Sweden. On the other hand, these two countries differ in institutional arrangements of the housing markets, and these differences might contribute to the size of apartment price elasticities from changes in fundamentals. These arrangements include various banking sector policies, such as mortgage financing and valuation approaches, as well as different government regulations of the housing market as, for example, rent control. Originality/value: In distinction to the previous studies carried out on Swedish and German data for single-family houses, this study focuses on the apartment segment of the market and examines apartment price elasticities from a long term perspective. In addition, the results from this study highlight the differences between the two countries at the city level in an integrated long run equilibrium framework.

We study the long-term effects of macroeconomic fundamentals on apartment prices in major urban areas in Sweden and Germany. The panel cointegration analysis was chosen as the primary approach due to the limited availability of data for a more extended period and frequency. The dataset consists of 2 countries – Germany and Sweden. The Swedish dataset includes three major cities and a period of  23 years, while the German dataset includes 7 “Big cities” for 29 years. Pooling the observations allows overcoming data restrictions in econometric analysis of long-term time series such as spatial heterogeneity, cross-sectional dependence and non-stationary, but cointegrated data. The results lie in line with previous studies and also allow comparison of single city estimations in an integrated equilibrium framework. The empirical results indicate that apartment prices react much stronger on changes in fundamentals in major Swedish cities than in German ones despite quite similar underlying fundamentals. Comparative analysis of regulations on the rental market, bank lending policies, and approaches to valuation for mortgage purposes in these two countries provide evidence that this overreaction arises due to institutional differences in form bank lending policies, mortgage valuation practices, and regulations on the rental market. Application of the more sustainable value concept such as mortgage lending value in mortgage valuations could make lending for housing less procyclical and stabilize house prices over the long run. Moreover, it will help to keep house prices away from overreaction on changes in macroeconomic fundamentals.

New residential construction in many countries with rapid urban growth is often interrelated with smaller housing units being built. Sweden is not an exception. It is of interest to investigate the driving forces behind this tendency. Our presumption is that municipal land price policies and building permit regulations might play a certain role in this process. Contrary to previous studies that focus on the number of new dwelling units in housing construction, our purpose is to analyze the average size of new housing units and the factors that affect it on an aggregate level. We apply seemingly unrelated regressions for analysis of the average apartment size in new residential construction in the three largest metropolitan regions in Sweden as a function of the changes in population, apartment rent and prices, mortgage interest rates, land prices, and building permits per capita as a proxy for regulation. The unbalanced panel dataset includes the period between 1998 and 2017 and covers both the rental and the housing cooperative sectors. The analysis demonstrates that land prices and building policies along with market fundamentals are the underlying factors that affect the average size of an apartment in new residential construction in Sweden.

We can observe a trend of smaller housing units in new residential construction in many countries with rapid urban growth. It is of interest to recognize the driving forces that are behind this trend because if it sustains in the long run, as both positive and negative consequences for sustainable development of the housing market can be expected. Therefore, the purpose of this paper is to examine the main underlying factors affecting the average size of apartments in new construction in metropolitan areas in Sweden. Panel data methodology applied in the analysis. The model uses the average size of an apartment in new housing construction as a function of the changes in the population, apartment prices, mortgage interest rates, rents, land prices, building permits per capita. We estimate the interrelationship between apartment size and these variables through simultaneous equation models. The data covers both the rental and the cooperative housing sectors over 24-28 years depending on availability of variables. The analysis demonstrates that the land values and building policies, along with market fundamentals, are the underlying factors that affect the average size of an apartment in new residential construction. Opposite to previous studies that focus on explaining the number of new housing, we analyze the average size of new housing units and factors that affect it on an aggregated level.

Since the size of the homeownership ratio differs significantly between countries, it is important to understand the mechanisms that lie behind decrease or growth of certain sectors of the housing market like rentals and housing cooperatives. The aim of this study is to analyze the long-term dynamics of the new residential supply in Sweden’s three largest cities for the period of 1990-2020 and estimate in what way market fundamentals affect it through new construction and housing conversions. We apply panel data methodology and, in distinction to previous research, consider the development of the housing market (urban growth) as physical volume. The results demonstrate that structural changes are driven mainly by fundamental demand factors and that the displacement effect occurs primarily in the market’s rental sector and not in the owner-occupied segment. The apartment price per square meter, together with mortgage interest rates, are the major driving factors in the process of converting dwellings into housing cooperatives. Fundamental variables that affect new construction in both the rental and housing cooperative sectors are population and income growth. In the presence of a rent control environment, the rent or price level does not contribute to adding new units to the total housing stock. 

Since the size of the homeownership ratio differs significantly between countries, it is important to understand the mechanisms that lie behind the decrease or growth of certain sectors of the housing market such as rentals and housing cooperatives. The aim of this study is to analyze the long-term dynamics of the new residential supply in Sweden's three largest cities for the period of 1990-2020 and estimate in what way market fundamentals affect it through new construction and housing conversions. We apply panel data methodology and, in distinction to previous research, consider the development of the housing market (urban growth) as physical volume. The results demonstrate that structural changes are driven mainly by fundamental demand factors and that the displacement effect occurs primarily in the market's rental sector and not in the owner-occupied segment. The apartment price per square meter, together with mortgage interest rates, are the major driving factors in the process of converting dwellings into housing cooperatives. Fundamental variables that affect new construction in both the rental and housing cooperative sectors are population and income growth. In the presence of a rent control environment, the rent or price level does not contribute to adding new units to the total housing stock.

Purpose – This paper aims to examine tenure choice in the Swedish housing market with explicitconsideration of households’ credit constraints in combination with age and ethnic background.

Design/methodology/approach – Observations of some 940,000 households were used to analyse theStockholm housing market in 2008, prior to the implementation of the mortgage cap. The tenure choicemodels were estimated using a two-stage instrument variable (IV) logit and probit model with ownership orrenting as outcome.

Findings – The results suggest, as expected, that being financially restricted is negatively related toowning. In particular, financial restriction is more binding for young households and households with aforeign background than for other types of households. These two sub-groups are also known to havedifficulties establishing themselves in the rental housing market, and are therefore specifically vulnerable tofurther financial constraints such as borrowing restrictions or amortization requirements.

Originality/value – The government in Sweden has become concerned with the rapid growth inhousehold indebtedness. As a response, a 0.85 loan-to-value ratio mortgage cap was introduced in 2010.However, critics are concerned with the effects this may have on the possibility for certain households topurchase a dwelling.

Objective: The aim of this study is to describe patients with heart failure and an ejection fraction (EF) of more than or equal to 40%, managed in both Primary- and Hospital based outpatient clinks separately with their prognosis, comorbidities and risk factors. Further to compare the heart failure medication in the two groups. Design: We used the prospective Swedish Heart Failure Registry to include 9654 out-patients who had HF and EF >= 40%, 1802 patients were registered in primary care and 7852 in hospital care. Descriptive statistical tests were used to analyze base line characteristics in the two groups and multivariate logistic regression analysis to assess mortality rate in the groups separately. Setting: The prospective Swedish Heart Failure Registry. Setting: The prospective Swedish Heart Failure Registry. Subjects: Patients with heart failure and an ejection fraction (EF) of more than or equal to 40%. Main outcome measures: Comorbidities, risk factors and mortality. Results: Mean-age was 77.5 (primary care) and 70.3 years (hospital care) p < 0.0001, 46.7 vs. 36.3% women respectively (p < 0.0001) and EF >= 50% 26.1 vs. 13.4% (p < 0.0001). Co-morbidities were common in both groups (97.2% vs. 92.3%), the primary care group having more atrial fibrillation, hypertension, ischemic heart disease and COPD. According to the multivariate logistic regression analysis smoking, COPD and diabetes were the most important independent risk factors in the primary care group and valvular disease in the hospital care group. All-cause mortality during mean follow-up of almost 4 years was 315% in primary care and 27.8% in hospital care. One year-mortality rates were 7.8%, and 7.0% respectively. Conclusion: Any co-morbidity was noted in 97% of the HF-patients with an EF of more than or equal to 40% managed at primary care based out-patient clinics and these patients had partly other independent risk factors than those patients managed in hospital care based outpatients clinics. Our results indicate that more attention should be payed to manage COPD in the primary care group.

The aim of this study was to describe gender-related differences in characteristics and mortality in heart failure (HF) patients managed in primary care (PC). We included 1802 hF patients aged 77.5 ± 8.8 years (47% women) with ejection fraction (EF) ≥ 40% from the Swedish Heart Failure (SwedeHF) registry. The patients were divided by gender, and by heart failure with mildly reduced ejection fraction (HFmrEF; EF 40–49%) or heart failure with preserved ejection fraction (HFpEF; EF ≥ 50%). Men included in the study were younger (mean-age 76.4 vs 78.7 years, p < 0.0001) and showed a higher age-adjusted mortality (p < 0.0001). Men more often showed ischemic heart disease, 49% vs. 38% (p < 0.0001), atrial fibrillation, 56% vs. 50%, and diabetes, 25% vs. 17% (both p < 0.01). Women had higher blood pressure compared to men (p < 0.01), more commonly had kidney dysfunction (p < 0.01), and a worse functional capacity (p < 0.01). Cardiovascular diseases were the dominating causes of death in men and women (60% and 56%) but were less dominating in individuals with EF ≥ 50%, especially among women (56% in men vs. 46% in women). Among women with EF ≥ 50%, the mortality was dominated of mixed and unspecified diseases. Other important causes of death were cancer (15%) and chronic obstructive pulmonary disease (13%). Men managed in PC with HF and EF ≥ 40% have a higher age-adjusted mortality than women. Cardiovascular disease is the dominating cause of death in both genders. Other frequent causes of death were malignant tumors and respiratory diseases, illustrating the need to carefully diagnose and treat all associated comorbidities.

Purpose - Customer interactions with sellers change as social interactions in society change. The old dichotomy between transaction and relation exchange may no longer be valid as customers form relationships with sellers in new ways. It is against this background that the authors study how customers' subjective perception of relational exchange appears in objectively defined transactional and relational exchange forms. The authors study one bank's customers, and, based on objective bank records, the authors identify segments that behave as transactional and relational customers. The authors also identify a group of customers who are in between transactional and relational, and the authors call these interimistic relational, since they interact repeatedly with the bank in a short period of time. The paper aims to discuss these issues. Design/methodology/approach - The authors study how subjective attributes of relational exchange differ in objectively defined transactional, interimistic, and relational customer groups. The authors use a large data set, consisting of a combination of survey and objective bank records for 90,528 bank customers. Findings - Findings are that the old dichotomy between transaction and relation is no longer valid, since customers' exchange behavior and perception of exchange do not match up when it comes to the transactionrelation dichotomy. The authors find empirical evidence for that the subjective relational attributes can be observed in objectively defined relational, interimistic, and transactional customer groups. Overall, subjective relational attributes are strongest in the objective relational group; they are weaker in the interimistic group. Relational attributes are weakest, but still present, in the transactional group. Practical implications - The findings presented here suggest strong support for relationship marketing practice, since even customers who behave transactionally perceive that they have an element of relationship with the seller. The authors find that customers may behave in a relational, interimistic, and transactional way, but that they perceive themselves as more or less relational. The practical implication is that customer analysis should focus on exchange forms, and that it is essential to analyze how exchange changes, and how multiple exchange forms may be combined in customer behavior and perception. Social implications - The social implications of this paper are that marketers should consider the exchange between customer and financial service supplier as more or less of a relationship, and more or less of a service. Financial service firm strategies and regulation of financial services should acknowledge that no financial service transaction is independent of the relationship between the financial service provider and the customer. It may seem so objectively, but subjectively, it is not. Originality/value - The authors present a unique comparison of objective and subjective customer exchange. There are two contributions that come from this research. The first is that customers perceive themselves as partially relational, even though they behave transactionally. The other contribution is that the authors identified interimistic relational exchange (IRE) as an exchange form in between relational and transactional. IRE can potentially be very important for market research and practice, as it captures modern market behavior. In today's world, consumers form their perceptions in a multitude of ways, and may therefore have relational attitudes and transactional behaviors. More research is needed into how consumer perceptions and behaviors relate to each other, and how it impacts consumer purchase of financial services.

Purpose: The purpose of this paper is to determine how three relational attributes – duration, context and trust – are subjectively perceived by bank customers, and how these affect their saving behavior, as defined by monthly flows to mutual funds and the financial products bought and held in stock. Design/methodology/approach: The authors use a combination of unique bank register and subjective survey data, and a structural equation model for theory development. Four constructs are developed to estimate the structural model, i.e. saving behavior, duration, context and trust. Findings: The authors find that all three relational attributes have positive effects on saving behavior. The authors also find that duration and context have the largest total effects, and that trust is a mediating variable channeling indirect effects from context and duration to saving behavior. Practical implications: One implication for bank managers is that it takes time and understanding of customer context to gain customer trust, but that this increases customer savings. Another implication is that the authors confirm that relational attributes can be studied using subjective measures in surveys, and that these have an effect on objective savings behavior. The findings provide an understanding that could develop both the customer’s value and the banks’ business opportunities. Originality/value: The impact of relationships between bank advisors and their customers in terms of costs and benefits has been studied, but a little research has focused on the attributes of the relationship and how these affect customers’ saving behavior. The study also uses unique objective bank register data, combined with customers’ subjective perceptions of the relationship.

Purpose: The purpose of this paper is to develop a model of bank advisor/customer relationships and customer saving behavior. Design/methodology/approach: The research is a theoretical review and model development of savings behavior and bank advisor/customer relationships. The review is used for the development of a model of bank advisor/customer relationships, and their effect on savings behavior. Findings: Findings are a model that distinguishes three kinds of exchange (relational, interimistic, and transaction) in between bank advisor and customer. The three kinds of exchange then influence customer savings behavior. Research limitations/implications: The implications of this research is that it points to that relationship marketing theory can be used in the analysis of how bank advisors influence customer savings behavior. Practical implications: For regulators and financial services firms, these findings point to how the role of bank advisors for consumer savings behavior can be analyzed. This is important, as much policy work presumes that advisors influence customer savings behavior, but the knowledge base for that presumption needs to be better understood. Social implications: The paper contributes toward a better understanding of the social exchange between bank employees and customers as regards savings products. Originality/value: This paper is original because it includes many theoretical research fields, and because it connects the bank advisor and customer relationship with the customer's savings behavior.

Purpose - This paper investigates the viability of the relationship-oriented business model. Specifically, it examines the effects of bank customers' satisfaction, loyalty, and trust in bank advisors on two client-level performance measures; client-level non-interest revenue, and client-level revenue on net interest spread. It further investigates how effects are moderated by differences in clients' risk tolerance and financial literacy. Design/methodology/approach - The findings are based on analyses of a data set that combines survey data (collected from 13,525 bank clients in 2013) with bank record data from each respondent. The cross sectional data is analyzed using OLS-regression and structural equation modeling. Findings - Overall, the findings are that the relationship banking model generates non-interest revenue, but not revenue on net interest spread. In more detail, findings show that trust has a positive direct effect on client-level non-interest revenue. Furthermore, trust mediates the entire effect of satisfaction and loyalty on client-level non-interest revenue. Customer satisfaction and loyalty do not lead to enhanced client-level non-interest revenue if there is little trust in bank advisors. Findings further show that the relevance of trust for non-interest revenue is higher for clients with high risk tolerance and high financial literacy. Satisfaction, loyalty, and trust have no effect, however, on client-level revenue on net interest spread. Originality/value - While previous literature mainly has used subjective intentions (e.g., repurchase behavior) as operationalization of performance, this paper combines subjective survey data and objective performance data, allowing the investigation of how the customer relationship model affects actual performance. Furthermore, the paper investigates the relational banking model's effect on non-interest and net interest spread revenue, and we show that the relational banking model generates only non-interest revenue, and not net interest spread revenue. The fine-grained client-level data also allows the investigation on how the effect of trust on client-level performance differs among client groups with different cognitive characteristics (i.e., risk tolerance and financial literacy).

This paper investigates the viability of the relationship-oriented business model of the bank advisory service function and tests its viability considering differences in clients’ risk tolerance and financial literacy. Specifically, it investigates the effects of bank customers’ satisfaction, loyalty, and trust in bank advisors on two client level performance measures; client level non-interest revenue and client-level revenue on net interest spread. It further investigates how effects are moderated by differences in clients’ risk tolerance and financial literacy. The findings are based on analyzes of a data set that combines survey data (collected from 13,525 bank clients in 2013) with bank record data from each respondent. The cross sectional data is analyzed using OLS- regression and structural equation modeling. Findings show that trust has a positive direct effect on client level non-interest revenue. Further, trust mediates the entire impact of satisfaction and loyalty on client-level non-interest revenue. Customer satisfaction and loyalty do not lead to enhanced client-level non-interest revenue if there is limited trust in bank advisors. Findings further show that the relevance of trust for non-interest revenue is higher for clients with high risk tolerance and high financial literacy. Satisfaction, loyalty, and trust have no effect, however, on client-level revenue on net interest rate spread.

Background: Cardiovascular disease is a serious complication in patients with dysglycaemia, defined as either type 2 diabetes or impaired glucose tolerance. Research focusing on the identification of potential markers for atherothrombotic disease in these subjects is warranted. The antiphospholipid syndrome is a common acquired prothrombotic condition, defined by a combination of thrombotic events and/or obstetric morbidity and positivity of specific antiphospholipid antibodies. Available information on antiphospholipid antibodies in dysglycaemia is scarce. Objective: This study investigates the association between antiphospholipid antibodies and dysglycaemia. Patients/Methods: The PAROKRANK (periodontitis and its relation to coronary artery disease) study included 805 patients, investigated 6-10 weeks after a first myocardial infarction, and 805 matched controls. Participants without known diabetes (91%) underwent an oral glucose tolerance test. Associations between antiphospholipid antibodies (anti-cardiolipin and anti-beta 2 glycoprotein-I IgG, IgM and IgA) and dysglycaemia were analysed. Results: In total, 137 (9%) subjects had previously known type 2 diabetes and 371 (23%) newly diagnosed dysglycaemia. Compared with the normoglycaemic participants, those with dysglycaemia had a higher proportion with first myocardial infarction (61% vs 45%,p < 0.0001) and were more often antiphospholipid antibody IgG positive (8% vs 5%;p = 0.013). HbA1c, fasting glucose and 2-h glucose were significantly associated to antiphospholipid antibody IgG. Odds ratios (ORs) were 1.04 (95% confidence interval [CI] 1.02-1.06), 1.14 (95% CI 1.00 - 1.27) and 1.12 (95% CI 1.04 - 1.21), respectively, after adjustments for age, gender and smoking. Conclusions: This study reports an association between antiphospholipid antibody IgG positivity and dysglycaemia. Further studies are needed to verify these findings and to investigate if antithrombotic therapy reduces vascular complications in antiphospholipid antibody positive subjects with dysglycaemia.