A factor analysis of landlords’ and retail trade tenants’ different beliefs in lease negotiations: evidence from the Swedish market

Abstract

Purpose

The purpose is to increase knowledge of rent negotiations by investigating differences in beliefs held by property landlords and retailers on factors that they deem important in rent negotiation.

Design/methodology/approach

This study investigates differences in subjective beliefs held by landlords and retail trade tenants on factors that affect rent levels during the rent negotiation process using a factor analysis approach. Semi-structured interviews were made with seven large real estate owners/landlords and retailers and eight experts in negotiating retail rent to elicit variables that have an impact on retail rent. Thereafter, a web-based survey was sent to 421 respondents who had experience in rent negotiation. Several factors were extracted using factor analysis. The data collection was made in Sweden during the coronavirus disease 2019 (COVID-19) pandemic in late spring 2021

Findings

Significant differences are found in beliefs held by landlords and retail trade tenants in four out of seven-factor: regional growth, e-commerce, customer focus and trust. Landlords rate these factors higher than retailers do. There are also systematic differences between landlords and retailers depending on their education levels on the following factors: rent and vacancies, e-commerce and customer focus. The number of years of experience did not prove to be significant instead differences are found to exist in factors

Research limitations/implications

Not only do traditional factors of importance, such as lease structure, the effect of location, size and anchor or non-anchor tenants, have an effect on negotiated rent levels. Differences in other factors also exist, such as regional growth, e-commerce, customer focus and trust factors that may play an important role in the negation of retail rent.

Practical implications

The findings provide new insights into the different views on factors that affect rent negotiations between landlords and retail tenants. Knowledge of such differences may increase the overall transparency in the negotiation process. Transparency may be increased by putting forward information on these factors before a negotiation takes place, in order to smooth differences in their beliefs.

Social implications

If transparency in the negotiation process of retail rent increases, time to reach an agreement, stress and anxiety can be reduced by putting forward information on factors where differences exist between landlords and retailers

Originality/value

New insights on retail rent negotiation have been put forward in this research paper. Not only do traditional factors such as lease structure matters, but subjective beliefs on factors such as regional growth and the level of education are also important, as this study has shown using a factors analysis approach.

Citation

Lundgren, B.A., Hermansson, C., Gyllenberg, F. and Koppfeldt, J. (2023), "A factor analysis of landlords’ and retail trade tenants’ different beliefs in lease negotiations: evidence from the Swedish market", Journal of European Real Estate Research, Vol. 16 No. 2, pp. 238-257. https://doi.org/10.1108/JERER-08-2022-0023

1. Introduction

Previously, an extensive amount of research have been conducted on, e.g. lease structure, rent determinants, and the effect of location, size, and anchor or non-anchor tenants as factors that affect rent levels (Brueckner, 1993; Carter and Vandell, 2005; Lee, 1995; Miceli and Sirmans, 1995; Sirmans and Guidry, 1993; Wheaton, 2000). Despite previous research efforts, it is not well-known how landlords and retail tenants’ beliefs differ in the negotiation phase of retail rent. Our study will add to the knowledge of the real estate research community and to practitioners, by investigating how retailers’ and real estate landlords’ views differ in Sweden during the coronavirus disease 2019 (COVID-19) pandemic on factors they deem important in negotiating rent by investigating differences in their beliefs. At this stage, our results do not demonstrate causality. Causality as given by a formal structural equation model would be beyond the scope of this explorative research study.

To inform us about critical variables in a rent negotiation, we have conducted a qualitative study using semi-structured interviews. Seven managers being responsible for negotiating retail rent as landlords and tenants participated in the interviews. In addition, interviews were also made with an advisory board, consisting of eight professionals with experience of rent negotiation. A web-based survey was then sent to respondents negotiating retail rent in Sweden to extract information on the variables that were found to be important in deciding retail rent in late spring 2021.

We used principal components (Berry, 1971; Kline, 1994; Tabachnick and Fidell, 2014) to discover which variables in the set that form independent factors. Principal component analysis (PCA) allows us to reduce the number of variables seemingly uncorrelated to a fewer number of principal components explaining the variance in our dataset. PCA accomplishes this reduction by identifying directions where the variation in the data is maximal. We use PCA with communalities and varimax rotation, which allows us to represent and interpret complex data by a relatively small number of factors. The purpose of using PCA and statistical tests is to reveal differences that may exist between landlords’ and retail tenants’ beliefs about the process of negotiating retail rent. We hypothesize that landlords’ and retail tenants’ views on variables used to determine rent will differ, depending on past experiences and expectations of future changes in the economy.

The survey was sent to 421 respondents; 156 respondents answered the survey, resulting in 106 fully complete answers. Responses were measured on a seven-point Likert scale. Results from the PCA and statistical tests show that real estate landlords are prone to believe that factors such as regional growth are more important while negotiating rent than do retail tenants. Moreover, we also find differences in beliefs on the importance of e-commerce, perceived trust and customer focus; also, our sample reveals differences in their views that depend on education. Thus, a landlord determines these factors as more important in the negotiation phase than a retail tenant. Our findings give new insights into the complex process of negotiating retail rent. Seven underlying dimensions in the rent negotiation process were labeled: (1) regional growth, (2) rent and vacancies, (3) GDP growth, (4) e-commerce, (5) customer focus, (6) external information and (7) trust.

The study is organized as follows. Section 2 provides a literature review of relevant research on retail rent. In Section 3, we discuss our methodology and how the data were generated. Section 4 provides results, and, finally, in Section 5, we conclude our results.

2. Literature review

Resent research by Guven et al. (2022) discusses the effect of competition and concentration on retail rent. Yang et al. (2021) use information asymmetry theory to explain rent and vacancy adjustments. Nanda et al. (2021) discuss the potential impact of e-commerce and digitalization post COVID-19 pandemic. Pretorius and Cloete (2020) discuss the complexity of online sales on a turnover-based retail rent in South Africa. However, research on retail rent during the negotiation process conducted using factor analysis from a rent negotiation perspective between landlords and tenants has, to our knowledge, not been published earlier. Thus, there exists a research gap in factors that affect landlords’ and tenants’ different views during rent negotiation. We hypothesize that landlords’ and retail tenants’ views on variables used to determine rent will differ, depending on past experiences and expectations of future changes in the economy.

The following literature review will present previous research on rent determinations. From an agency perspective, Ross (1973) concluded that the optimal risk-sharing contract for a risk-averse tenant and a risk-neutral landlord is a pure turnover rent. However, in the presence of moral hazard, an optimal incentive contract is, instead, one including a fixed rent as well as a turnover rent. The level of turnover rent in relation to base rent has been thoroughly studied; it has been found that it is highly related to externalities, and lease structure can be considered helpful in reducing the conflict in interest between risk-averse tenants and risk-neutral landlords (Lee, 1995; Miceli and Sirmans, 1995; Brueckner, 1993). Furthermore, according to agency theory, anchors and non-anchors affect sales, and lease contracts should be constructed to stimulate desired actions from all relevant parties. Thereby, contracts are written certain ways to internalize externalities, using both base and turnover rent, resulting in optimal allocation of space in shopping centers (Gould et al., 2005).

Sirmans and Guidry (1993) pointed out four main determinants affecting rent negotiation and market rent: customer-drawing power, design/configuration of the property, locational characteristics and market conditions. Customer-drawing power is, in turn, substantiated by the size and is positively correlated to the level of the rent. Furthermore, customer-drawing power and rent levels are affected by age, implying that the age is negatively correlated to the market rent. A third factor affecting the customer-drawing power is the type of anchor tenants. Nationally well-known retailers are known as traffic attractors, having a positive effect on the drawing power and, hence, increasing the market rent. The design/configuration, according to Sirmans and Guidry (ibid), has an impact on rental levels. By design or configuration, the authors do not mean design in architectural terms, but rather how the center is built and how the tenants are positioned. It is, according to the authors, important to focus on having anchor tenants in places with high visibility and, in that way, attracting traffic to the anchor tenants as well as to other tenants. An optimized disposition of the tenants has a positive effect on the rental level. When investigating what effect the locational attributes and market condition have on the rental level, some main subfactors, such as population density, growth potential and income in the area, are argued to be the main locational attributes that have a positive correlation with the rental level. The amount of vacant space and general economic conditions have a substantial effect on the outcome of rent negotiation.

Research has shown that rent levels differ greatly depending on the type of tenant. Gould et al. (2005) carried out quantitative research to define the difference in rental levels between anchor tenants and non-anchor tenants. The first hypothesis was that anchor tenants received discounts on rents due to their ability to draw customer traffic, not only to their own stores but also to the other stores. Their second hypothesis was that non-anchor tenants paid rents at a premium. The regression model was based on approximately 2,500 contracts in the United States, with anchor tenants representing 58% of the leasable area, but accounting for only 10% of the rental revenue.

The fact that anchor tenants, or customer-drawing tenants, generally enjoy what could be viewed as rent subsidies is often referred to as rent discrimination. Despite its discriminating nature toward smaller tenants, rent discrimination is widely recognized and considered fully rational from a property owners’ perspective (Williams, 2014). Furthermore, Gatzlaff et al. (1994) have argued for the importance of anchor tenants. Their results showed that the loss of an anchor tenant lowered the customer-drawing power, increased the overall vacant space (not including the anchor tenant space) and, hence, decreased the rental level.

The store location factor and its effect on retail sales and, in extension, retail rent have been widely studied using multiple approaches. Carter and Vandell (2005) applied bid rent theory, indicating that there is a relationship between rent and distance to the center of the shopping mall. At a district or regional level, the trade-off between location and retail agglomeration (i.e. clustering) has been studied, with results showing that clustering is of greater importance for explaining consumer patronage than location (Eppli and Shilling, 1996). In a context of ground-floor commercial activities, the importance of centrality for retail concentration has been established, where centrality is defined as a function of closeness, betweenness and straightness (Porta et al., 2009). These determinants are still relevant and judged to have an impact on rental levels in the negotiation process. E-commerce and its effect on retail leases have been, at least partly, studied. As retailers shift focus from on-site to off-site sales, the role of the physical store is going through a great change, and, as part of that change, revenue is being transferred from physical stores to online stores. This transformation is also subject to changes in the parameters going into a rent negotiation. As the purpose and business models of physical retail change, lease agreements need to be altered, as turnover rent is a lease structure better fit for the past than the future (Baen, 2000; and Miller, 2000). Gyllenberg and Koppfeldt (2020) focused on the negotiating gap between landlords and tenants in the retail sector and found that the gap between the two actors was significant, not least since the growth in e-commerce is complicating rent negotiations.

2.1 Our objective

The literature review has shown that previous research on rent and rent negotiations has focused on lease structure, rent determinants, the effect of location, size and anchor or non-anchor tenants, and the effects of e-commerce. Our objective is to increase knowledge of rent negotiations by investigating differences in beliefs held by property landlords and retailers on factors that they deem important in rent negotiation. Our first research question is thus, how do landlords’ and tenants’ beliefs on factors deemed to be important differ when they are negotiating rent?

According to Huffman (1974), the rate of adjustment in decision-making is positively correlated to the level of education. Decision-makers with higher education more quickly grasp changes and more quickly and accurately make adjustments. Brooks and Williams (2022) found that decisions of respondents facing an action to sell or keep their investments in a downturn in the economy are affected by the highest level of education of the respondents. Respondents having a higher level of education were positively correlated with keeping their investments in the downturn. Differences exist in susceptibility to behavioral biases due to education, with those having a higher level of education being more confident when making investment decisions (Jamshidinavid et al., 2012). Positive relationships between an individual’s educational level and their degree of risk tolerance have been observed (Chang et al., 2004). We will further investigate if differences in scores on the seven factors may depend on the respondents’ educational levels. Our second research question is, do scores on the seven factors solution vary depending on educational levels?

3. Method and data

3.1 Factor analysis

Factor analysis using principal components, or PCA, is explorative in nature and is commonly used for investigating whether an underlying pattern exists in a set of data Berry (1971. PCA allows us to reduce the number of variables seemingly uncorrelated to a fewer number of principal components that explain the variance in our dataset. PCA accomplishes this reduction by identifying directions, called principal components, where the variation in the data is maximal. Thus, variables that are correlated with each other but are largely independent of other subsets of variables are combined into factors (Tabachnick and Fidell, 2014). Factors are thought to reflect underlying processes that have created the correlations among the variables. Principal components, or eigenvalues, can be plotted, making it possible to determine whether samples can be grouped into a number of factors (see Figure 1). However, factor analysis is not without problems. One of the drawbacks is the inability to test the solution as in regression and logistic analysis; another problem is the infinite number of available rotations. PCA is an effective tool in data reduction in the early stages of research to investigate relationships among variables (Kline, 1994).

3.2 Interviews

The aim of the seven semi-structured interviews was to gain the landlords’ and tenants’ point of view on determinants of rent for retail premises as an input to our quantitative survey (see Appendix A1 for a list of the companies participating). All of the interviewees are active in the leasing of retail premises in one way or another. The respondents were selected due to their expertise across multiple cities and regions in Sweden, as well as in managing lease agreements (see Table A1). The interviewees had a close focus on lease structure and how to construct an optimal contract from their different perspectives as landlords and tenants. In addition, interviews were also made with our advisory board, consisting of eight professionals with long experience of rent negotiation. The interviews showed that, although the rent determinants covered in previous research are still relevant in determining rent, landlords and tenants indicated that other factors might have considerable impact as well. Both parties could see, first, that the future calls for shorter lease terms and more flexibility than has been appropriate historically. Second, expectations about the future have a large impact on the outcome of rent negotiations, and the existing discrepancy between retailers and real estate owners is mostly dependent on a gap in their expectations. It became obvious that real estate owners use the conventional rent determinants to determine rent levels. Simultaneously, retailers did not put much effort into recognizing these factors. Retailers expressed that the profitability of a store is the main factor in determining whether the rent level is reasonable for a particular store in a rent negotiation.

Real estate owners and retailers agree that real estate values are limiting real estate owners’ willingness and possibilities to reduce rents. Similarly, retailers are limited in their willingness and possibilities to pay rents above certain levels of the occupancy cost ratio (OCR). Landlords’ and tenants’ different expectations in rental negotiation widen the gap between retailers and real estate owners. In order to narrow this gap long term, there is an obvious need for greater transparency and joint efforts in predicting future developments in factors that have an impact on rent negotiation. To further widen our perspective on the negotiation process, an advisory board was added, consisting of eight economists and consultants with experience in rent negotiations. The board advised on variables that affect rent decisions in practice and gave their points of view on rent negotiation. The board also participated in validating survey questions, as well as in pre-testing the web-based survey before sending it out to respondents.

3.3 The survey

Our web-based survey consisted of 37 questions, of which 22 questions were measured on an ordinal seven-point Likert scale ranging from completely disagree to completely agree. One example is the following question: The negotiated rent is affected by how the regional economy has developed during the past year.

We also included questions about socioeconomic factors, such as gender, age, the level of education and the type of occupation as landlord or retailer. Questions were asked about the respondents’ experience in negotiating rent in years and the typical size of the city – large/medium or small – where the respondent negotiated rent to capture specific details of the respondent. The importance of anchor tenants and current rent levels as potential anchor values was included in the survey. Questions about the importance of gross domestic product (GDP) growth and regional growth, the effect of e-commerce on rent levels and various sources of information used during the negotiation phase were also included. Furthermore, we asked whether market value and vacancies have an effect when rent is determined. Trust in the negotiating part and trust in legal institutions were also included in the questionnaire. The effect of COVID-19 on the turnover was included, as well as whether subsidies were received, in order to capture information on the effects of the ongoing pandemic on business. For summary statistics and questions used in the web-based survey, see Table 1.

3.4 Data

Contact information about the respondents were received from the following representative organizations for landlords and retailers in Sweden: HUI Research (a consultancy for retail and property management), City i Samverkan (a company facilitating cooperation between private and public organizations in city areas), Föreningen FFE (an organization for economists in the real estate sector), Public Housing Sweden (SABO) and Unionen (a large trade union supporting white-collar workers in Sweden). Data to our study were collected from a data register managed and updated by these organizations. The respondents were selected from five different regions in Sweden. The largest group of respondents came from the Stockholm region, providing 36.2% of the respondents. The south region, including the city of Malmö, provided 19.8% and the mid-region of Sweden provided 18.8% of the respondents. The western region, including the city of Gothenburg, contributed 17.2%. The remaining 8.0% of the respondents came from the northern part of Sweden. A total of 55.8% of the respondents categorized themselves as mostly negotiating rent in a large city; 38.8% negotiated rent in a mid-sized Swedish town, and 5.4% in a small town. When asking about the location of a typical store where the respondent is negotiating rent, 40.5% state that a typical store is located in a local street environment. A total of 44.5% of the respondents mostly negotiate rent in a retail center. Approximately 15.0% of the respondents were typically negotiating rent in an external trade area.

The web-based questionnaire was sent to respondents during the late spring of 2021. A reminder was sent to those who did not answer after two weeks, and a second reminder after three weeks. In total, we reached 421 respondents who had experience in rent negotiation. In total, 156 respondents (57 landlords and 99 retail tenants) answered the questionnaire; however, only 106 were complete, leading to a response rate of 25.2%. Since our sample is limited in size, our results should be viewed only as a starting point in exploring differences in beliefs between landlords and tenants.

The survey was answered by 36 females (32%) and 80 males. The mean age was 48 years (min = 24 and max = 74 years). Of the respondents, 56% had between 7 and 25 years of experience in negotiating rent. Also, 64% classified themselves as tenants and 36% as real estate landlords. Education has four categories for highest finalized education (primary school, secondary school, three–four years of university, and five years or more education. There are differences in our sample in the education levels of landlords and tenants (see Table 2). Respondents identified as landlords have a higher educational level (mean = 3.19) than do those who classified themselves as tenants (mean = 2.97). An independent t-test showed that the difference between educational levels was statistically significant (t = 1.95, df = 106, p < 0.10, two tailed). We will further investigate differences in the importance of education on different factors, depending on educational level, using factor analysis and independent t-tests of mean differences. See Table 2 for an overview of differences in education levels.

4. Results

A factor analysis was made using SPSS Statistics 25.0. The cumulative variation (PCA, varimax rotation) by seven factors was 67.7% (see Table 3). The rotated factor solution and potential factors are found in Figure 1 and Table A2. The criteria for determining factors are an eigenvalue equal or higher than one. The factors can be thought of as representing regional growth, rent and vacancies, GDP growth, e-commerce, customer focus, external information and trust (see Table A3). An independent t-test and analysis of varience (ANOVA) are used for statistical analysis.

PCA with varimax rotation revealed a potential factor solution with eigenvalue (see Table 3 for the total variance explained by the six-factor structure).

During the interpretation of the rotated factor solution, an additional factor was added for conceptual reasons (Spector, 1992). Three variables from factor 1 were thus entered into a seventh factor named customer focus. The Kaiser–Mayer–Olkin test and Bartlett test of the suitability of the correlation matrix for factor analysis showed that the data set was factorable (>0.72, p < 0.000). Each factor was analyzed for reliability using Cronbach’s alpha, and the internal consistency of all of the factors is above recommended levels, except the factor for external information (Cronbach, 1951). The following reliability values apply to the seven-factor solution: factor (a), regional growth = 0.87; factor (b), rent and vacancies = 0.69; factor (c), GDP growth = 0.82; factor (d), e-commerce = 0.93; factor (e), customer focus = 0.76; factor (f), external information = 0.51 and factor (g), trust = 0.79. Factor (f), external information, has questionable reliability since it is below 0.7 (Nunnally, 1978). The correlation between the factors is found in Table A4. Correlation analysis shows that the factors have low inter factor correlation (no correlation between factors exceeds 0.61), which means that there are no multicollinearity problems between the factors.

5. Analysis

The literature review has presented factors that affect retail rent levels, such as lease structure, rent determinants and the effect of location, size, and anchor or non-anchor tenants. Our first research question concerned how landlords’ and tenants’ beliefs differ in factors deemed to be important when negotiating rent. We analyze differences in means using independent t-tests. Our results show that property landlords and tenants put different weights on the factors found in our study. Real estate landlords agree, to a greater extent, based on a seven-point Likert scale, that factors relating to regional growth, e-commerce, customer focus and trust affect the negotiated rent to a higher degree than do tenants. This means that landlords and tenants don't disagree on factors such as rent and vacancies, GDP growth and external information. Landlords and tenants put the same weight on the importance of these latter factors when negotiating rent. Statistical results in mean values and t-tests between landlords and tenants are found in Tables A5–A6.

Our statistical results show that landlords have a significantly higher mean value on the factor regional growth (mean = 4.70, t = 2.22**) than do tenants (mean = 4.26). Four questions in the survey capture how the regional economy developed over the past years and how it will develop in the current and next years, and how the industry developed over the past years and how it will develop in the current and next year. Since their mean values differ significantly, landlords are likely to value the factor regional growth higher than do tenants during a negotiation of rent.

We find a significant difference in mean values also for the factor e-commerce (mean = 4.14 for landlords, and mean = 3.45 for tenants, t = 2.66**). The questions concerning e-commerce in the questionnaire are framed to determine whether the negotiated rent is affected by the development of e-commerce during the past year, as well as for the current and next year. Differences in mean values show that landlords believe that this is the case to a greater extent than tenants do.

There are also significant differences for the factor customer focus (landlords’ mean = 4.92 and tenants’ mean = 4.51, t = 2.16**). The questions in the questionnaire are framed to determine whether the negotiated rent is affected by changes in consumer behavior and proximity to anchor tenants, as well as by the tenant mix in the past year, and for the current and next year. The differences indicate that landlords are likely to value the factor customer focus higher than tenants do during a negotiation of rent.

The perceived trust (the factor trust) between landlords and tenants turned out to be significantly different (landlords’ mean = 4.59 and tenants’ mean = 4.13, t = 2.28**). The three questions in the questionnaire are framed to determine whether trust is given in confidence between the parties. Differences in mean values in trust between landlords and tenants show that landlords on average have greater trust in their counterpart during a negotiation than tenants do.

Our second research question concerns whether differences exist in educational levels on factors that are deemed as important to landlord and tenants. From previous research on decision-making, we expect to find differences in beliefs depending on educational difference, see Table 4. We do find significant educational differences on the following factors: rent and vacancies, e-commerce and customer focus. Testing for significant differences on the factor level, we find that those respondents with a five-year or higher university degree have a significantly higher mean value on the factor rent and vacancies (mean = 5.2, t = 2.68**) than do those respondents having a gymnasium degree (mean = 4.44). We see the same result in the factor e-commerce: respondents having a five-year or higher university degree (mean = 4.15, t = 2.57**) score higher than those respondents having a gymnasium degree (mean = 3.13). For the factor customer focus, we find a significant difference between a three- to four-year university degree (mean = 4.97, t = 2.50**) and a gymnasium degree (mean = 4.23). Regarding respondents having a three- to four-year university degree significant differences exist in the factors e-commerce and customer focus (see Appendix A7). Depending on if the highest educational degree is on the gymnasium level or at a five-year or higher university degree, there are significant differences in beliefs on the importance of these three factors. We also find significant differences between respondents having a three- to four-year university degree and those having a university degree of five years or higher on the factors rent and vacancies and external information (see Tables A7–A9).

Does higher education lead to different beliefs on factors deemed important in rent negotiation? As shown above, we discovered significant differences on the factors rent and vacancies, e-commerce and customer focus. In order to explore further differences in educational levels, we have added a table (Table A10) to show how landlords’ and retailers’ mean values differ on the seven factors, depending on their role and educational level. We specifically note that landlords and tenants with a 3–4 years’ university education rate regional growth higher than retailers do (mean landlords = 4.99 and mean tenants = 4.33). The factors rent and vacancies are rated higher by tenants having a 5-year or higher university degree than landlords do (mean landlords = 4.83 and mean tenants = 5.58). We also find that landlords with a gymnasium education rate the factor e-commerce higher (landlords’ mean = 4.13 and tenants’ mean = 0.2.73). The factor customer focus, which includes questions on the importance of anchor tenants and tenant mix, is rated higher by landlords having a gymnasium and 3–4 years university degree than retailers do (mean landlords = 4.7 and mean tenants = 4.05). Landlords having a 5-year or higher university degree rate the factor trust much higher (mean = 4.73) than landlords (mean = 3.78).

We control for the respondents’ number of years of experience in six different categories: 1–2 years, 3–6 years, 7–15 years, 16–25 years and >25 years of experience. We find a significant difference only in the factor GDP growth for respondents having 3–6 years’ experience, compared with those with >25 years of experience (t = 2.87**), as well as for those with 7–15 years of experience versus those with >25 years of experience (t = 2.56**). No other significant differences based on experience are found.

6. Conclusions

Previous research on factors that affect rent levels have focused on the importance of e.g. lease structure, rent determinants, and the effect of location, size, and anchor or non-anchor tenants. Even though our sample is small, new insights have been found regarding the importance of industry and regional growth, e-commerce and customer focus, as well as trust between landlords and tenants in the negotiating process. Landlords were found to put more weight on the importance of these factors than do tenants. Such differences are likely to have an effect on their standpoints during the negotiation process. Our results were found in the upturn of the Swedish economy from late spring into the fall of 2021, during the COVID-19 epidemic. During this phase, landlords were likely to put more weight on the importance of industry and regional growth than did tenants, thus explaining their more positive score on the seven-point Likert scale. Landlords place greater importance on the actual and expected turnover from e-commerce during negotiations than do tenants. This might be because tenants have a more informed view of the actual turnover from e-commerce than do landlords. This information gap may disturb the negotiations of a fair rent because of lack of a mutual understanding of revenue from e-commerce. We also find significant differences in trust between the parties, where landlords have a higher trust for the counterpart. This difference was not expected, and this gap in trust indicates that trust is a factor that should be in focus to smooth the negotiation process.

Previous research on the effect of educational levels on decision-making show that decision-makers with higher education more quickly grasp changes and adjust more quickly than those with lower education. Moreover, financial actions were found to be positively correlated with individuals who were more confident when making investment decisions, as well as having a higher level of risk tolerance. We also found differences in the importance of the categories of rent and vacancies, e-commerce and customer focus, depending on formal education; this led to significant differences between respondents having a gymnasium degree and those with a five-year or higher university degree. Such differences exist also between respondents having a three-four-year university degree and those having a five-year or higher university degree. This means that education levels matter, as they give different perspectives on various factors. For example, respondents having a five-year university education hold a different view on regional growth and rent and vacancies than do respondents having a gymnasium degree. In order to explore further differences in educational levels, we added a new table (Table A10) to show how landlords’ and retailers’ mean values differ on the seven factors, depending on their role and educational level. Meanwhile, controlling for the number of years of experience for respondents, only the factor GDP growth shows a significant difference in two out of the six age groups.

Our results show that not only have objective factors, such as lease structure, the effect of location, size, and anchor or non-anchor tenants, an effect on rent levels, but subjective factors, such as the importance of regional growth, also play a role in rent decisions. However, using PCA, we cannot discover the causes and effects of these factors during an actual negotiation, since that would require an additional experiment that goes far beyond the scope of this paper. From a practical point of view, our findings provide new insights into the different views that exist between landlords and retail tenants in a rent negotiation. Trade organizations, landlords and tenants can increase transparency by putting forward information on these factors before a negotiation takes place in order to smooth differences in their beliefs based on educational differences.

To use our findings, we suggest that retailers focus on those factors where landlords have a significant higher mean value to increase the possibility of reaching through with their arguments during a rental negotiation. Following this advice, we do not mean that one part is better informed, or that more information is the key to success; instead, information should be provided that is perceived to be important. Thus, it is more likely that landlords will listen to arguments that they themselves believe are important. From our results, landlords have a significantly higher mean value on, e.g. the factor regional growth (mean = 4.70, t = 2.22**) than tenants do (mean = 4.26). The Swedish economy from late spring into the fall of 2021, during the COVID-19 epidemic, was in an upturn in the business cycle. During this phase, landlords would have been likely to put more weight on the importance of regional growth than would tenants, to motivate increasing rents due to an expected rise in sales. Retail tenants should, in their turn, put forward information on how regional growth is likely to have less impact on their sales. Exactly how negotiations would play out using our findings have not been in focus in our research at this stage.

Figures

Figure 1

The rotated factor structure and potential factors

Table 1

Questions used in the survey and summary statistics for 106 respondents

	Mean	Std. Deviation	Minimum	Maximum
1. The negotiated rent is affected by how GDP has developed during the past year	3.44	1.52	1.00	7.00
2. The negotiated rent is affected by how GDP is expected to develop during the current and next year	3.51	1.44	1.00	7.00
3. The negotiated rent is affected by how the regional economy has developed during the past year	4.06	1.36	1.00	6.00
4. The negotiated rent is affected by expectations of how the regional economy will develop in the current and next years	4.20	1.36	1.00	6.00
5. The negotiated rent is affected by how the industry in which the store is located has developed during the past year	4.70	1.39	1.00	7.00
6. The negotiated rent is affected by how the industry in which the store is located is expected to develop during the current and next year	4.59	1.33	1.00	7.00
7. The negotiated rent is affected by how e-commerce has developed in the industry you have negotiated rent for during the past year	3.68	1.48	1.00	7.00
8. The negotiated rent is affected by how e-commerce in the industry you have negotiated rent for is expected to develop during the current and next year	3.70	1.51	1.00	7.00
9. The negotiated rent is affected by the location of the premises	5.65	1.12	1.00	7.00
10. The negotiated rent is affected by expectations of consumers’ buying behavior	4.57	1.33	1.00	7.00
11. The negotiated rent is affected by the OCR for the premises	4.39	1.45	1.00	7.00
12. The negotiated rent is affected by the current rent level in the premises	5.11	1.17	1.00	7.00
13. The negotiated rent is affected by expectations of the property's future market value	4.38	1.34	1.00	7.00
14. The negotiated rent is affected by previous vacancies on the premises	4.31	1.42	1.00	7.00
15. The negotiated rent is affected by expectations of future lower/higher vacancies	4.33	1.44	1.00	7.00
16. The negotiated rent is affected by information and statistics from, for example, Swedish Trade Institute, HUI and the Property Owners	3.67	1.39	1.00	7.00
17. The negotiated rent is affected by information about rents via colleagues in the industry	4.20	1.39	1.00	7.00
18.The negotiated rent is affected by the proximity to anchor tenants	4.72	1.22	1.00	7.00
19. The negotiated rent is affected by the tenant mix	4.66	1.25	1.00	7.00
20. I have confidence in the lease negotiation process based on laws and regulations	4.94	1.22	1.00	7.00
21. I have confidence in how my counterpart as an organization negotiates rents for our common good	3.98	1.30	1.00	7.00
22. I have confidence in how my counterpart as an individual negotiates rents for our common good	4.01	1.27	1.00	7.00
23. Gender (1 = female, 2 = male)	1.62	0.49	1	2
24. Year of experience	3.96	1.33	1	6
25. Town size (1 = large, 2 = medium, 3 = smal)	1.50	0.60	1.00	3.00
26. Type of location (1 = street, 2 = retail center, 3 = external)	1.75	0.70	1	3
27. Current OCR 1=< 2%, 13 = don’t know)	5.20	3.75	1.00	13.00
28. Corona efffect on rent (1 = no effect, 8 = high effect)	2.36	1.24	1.00	8.00

Note(s):.Variables 1–22 are measured on a seven-point Likert scale, where 1 = not agree at all and 7 = totally agree

Source(s): Author’s own creation

Table 2

Education and role in negotiation of rent for retail premises

		Role
		Tenants	Landlord
Education level	Gymnasium	30.8%	18.6%
	3 to 4-year university degree	47.7%	44.2%
	5-year or higher university degree	21.5%	37.2%
	Total	100.0%	100.0%

Source(s): Author’s own creation

Table 3

Total variance explained in the factor solution of the lease negotiation process between real estate landlords and tenants

Component	Eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	6.41	29.14	29.14	6.41	29.14	29.14	3.97	18.04	18.04
2	2.60	11.84	40.97	2.60	11.84	40.97	2.65	12.03	30.07
3	2.00	9.10	50.08	2.00	9.10	50.08	2.42	10.99	41.05
4	1.64	7.46	57.54	1.64	7.46	57.54	2.05	9.31	50.36
5	1.21	5.52	63.06	1.21	5.52	63.06	2.04	9.27	59.63
6	1.04	4.71	67.77	1.04	4.71	67.77	1.79	8.13	67.77

Source(s): Author’s own creation

Table 4

Mean values on factors and educational levels

What is your highest formal education		Mean	Std. Deviation	N
Regional growth	Gymnasium	4.17	1.34	28
	3 to 4-year university degree	4.59	1.16	50
	5-year or higher university degree	4.45	0.79	28
	Total	4.44	1.13	106
Rent and vacancies	Gymnasium	4.44	0.99	28
	3 to 4-year university degree	4.78	0.66	50
	5-year or higher university degree	5.15	0.84	28
	Total	4.78	0.84	106
GDP growth	Gymnasium	3.61	1.47	28
	3 to 4-year university degree	3.38	1.43	50
	5-year or higher university degree	3.48	1.13	28
	Total	3.47	1.36	106
e-commerce	Gymnasium	3.13	1.57	28
	3 to 4-year university degree	3.81	1.41	50
	5-year or higher university degree	4.20	1.27	28
	Total	3.73	1.46	106
External information	Gymnasium	3.96	1.19	28
	3 to 4-year university degree	4.00	0.95	50
	5-year or higher university degree	4.44	0.79	28
	Total	4.11	0.99	106
Customer focus	Gymnasium	4.23	1.35	28
	3 to 4-year university degree	4.72	0.87	50
	5-year or higher university degree	5.05	0.62	28
	Total	4.68	1.00	106
Trust	Gymnasium	4.39	1.42	28
	3 to 4-year university degree	4.21	0.92	50
	5-year or higher university degree	4.44	0.93	28
	Total	4.32	1.07	106

Source(s): Author’s own creation

Table A1

Description of interviewees

Landlord: Citycon is a listed real estate company traded on the Nasdaq Helsinki stock exchange. The company owns 40 shopping centers in 5 countries in the Nordics: Finland, Estonia, Sweden, Norway and Denmark

Landlord: Vasakronan is the largest real estate owner in Sweden, with 174 properties, holding office and retail premises in the four biggest cities

Landlord: Diös is a listed real estate company traded on the Nasdaq Stockholm stock exchange. The company owns commercial and residential properties in northern Sweden

Tenant: KICKS is the largest cosmetics chain in the Nordics, with a total of 250 stores in Sweden, Finland and Norway. The company has 135 stores in Sweden

Tenant: KappAhl is a large fashion chain, with a total of 350 stores in Sweden, Norway, Finland and Poland. The company has 171 stores in Sweden

Tenant: Stadium is one of the largest chains in sporting-goods, with 180 stores in Sweden, Finland, Norway and Germany

Tenant: Kjell & Company is a leading actor in the consumer electronics segment in Sweden, with more than 100 stores nationwide, as well as 20 stores in Norway

Source(s): Author’s own creation

Table A2

Rotated component matrix

	Component
	1	2	3	4	5	6
V1 The negotiated rent is affected by how GDP has developed during the past year				0.832
V2 The negotiated rent is affected by how GDP is expected to develop during the current and next year				0.879
V3 The negotiated rent is affected by how the regional economy has developed during the past year	0.749			0.338
V4 The negotiated rent is affected by expectations of how the regional economy will develop in the current and next years	0.826
V5 The negotiated rent is affected by how the industry in which the store is located has developed during the past year	0.752
V6 The negotiated rent is affected by how the industry in which the store is located is expected to develop during the current and next year	0.765				0.307
V7 The negotiated rent is affected by how e-commerce has developed in the industry you have negotiated rent for during the past year	0.361				0.799
V8 The negotiated rent is affected by how e-commerce in the industry you have negotiated rent for is expected to develop during the current and next year	0.319				0.795
V9 The negotiated rent is affected by the location of the premises	0.362	0.465	−0.425
V10 The negotiated rent is affected by expectations of consumers’ buying behavior	0.562
V11 The negotiated rent is affected by the OCR for the premises		0.457
V12 The negotiated rent is affected by the current rent level in the premises		0.482		0.361
V13 The negotiated rent is affected by expectations of the property’s future market value		0.356			−0.408	0.437
V14 The negotiated rent is affected by previous vacancies on the premises		0.691
V15 The negotiated rent is affected by expectations of future lower/higher vacancies		0.810
V16The negotiated rent is affected by information and statistics from, for example, Swedish Trade Institute, HUI and the Property Owners						0.831
V17 The negotiated rent is affected by information about rents via colleagues in the industry		0.416				0.569
V18 The negotiated rent is affected by the proximity to anchor tenants	0.517	0.340				0.378
V19 The negotiated rent is affected by the tenant mix	0.521	0.481
V20 I have confidence in the lease negotiation process based on laws and regulations			0.631		0.479
V21 I have confidence in how my counterpart as an organization negotiates rents for our common good			0.867
I have confidence in how my counterpart as an individual negotiates rents for our common good			0.880

Note(s): Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization

a. Rotation converged in 9 iterations

Extraction method: PCA; rotation method: Varimax with Kaiser normalization

Source(s): Author’s own creation

Table A3

Factors describing the negotiation process between landlords and tenants

Regional and industrial growth	Rent and vacancies	GDP growth	E-commerce	Customer focus	External information	Trust
The negotiated rent is affected by how the regional economy has developed during the past year	The negotiated rent is affected by the location of the premises	The negotiated rent is affected by how GDP has developed during the past year	The negotiated rent is affected by how e-commerce has developed in the industry you have negotiated rent for during the past year	The negotiated rent is affected by expectations of consumers’ buying behavior	The negotiated rent is affected by expectations of the property’s future market value	I have confidence in the lease negotiation process based on laws and regulations
The negotiated rent is affected by expectations of how the regional economy will develop in the current and next years	The negotiated rent is affected by the OCR for the premises	The negotiated rent is affected by how GDP is expected to develop during the current and next year	The negotiated rent is affected by how e-commerce in the industry you have negotiated rent for is expected to develop during the current and next year	The negotiated rent is affected by the proximity to anchor tenants	The negotiated rent is affected by information about rents via colleagues in the industry	I have confidence in how my counterpart as an organization negotiates rents for our common good
The negotiated rent is affected by how the industry in which the store is located has developed during the past year	The negotiated rent is affected by the current rent level in the premises			The negotiated rent is affected by the tenant mix	The negotiated rent is affected by information and statistics from, for example, Swedish Trade union, HUI and Property Owners	I have confidence in how my counterpart as an individual negotiates rents for our common good
The negotiated rent is affected by how the industry in which the store is located is expected to develop during the current and next year	The negotiated rent is affected by previous vacancies on the premises
	The negotiated rent is affected by expectations of future lower/higher vacancies	a

Source(s): Author’s own creation

Table A4

Correlations in the seven-factor solution

		Regional economy	Rent and vacancies	GDP growth	E-commerce	External information	Customer focus	Trust
Regional and industrial growth	Pearson correlation	1	0.358**	0.349**	0.535**	0.291**	0.614**	0.318**
	Sig. (2-tailed)		0.000	0.000	0.000	0.002	0.000	0.001
	N	106	106	106	106	106	106	106
Rent and vacancies	Pearson correlation	0.358**	1	0.151	0.312**	0.464**	0.528**	0.003
	Sig. (2-tailed)	0.000		0.111	0.001	0.000	0.000	0.973
	N	106	106	106	106	106	106	106
GDP growth	Pearson correlation	0.349**	0.151	1	0.171	0.166	0.286**	0.020
	Sig. (2-tailed)	0.000	0.111		0.072	0.079	0.003	0.836
	N	106	106	106	106	106	106	106
e-commerce	Pearson correlation	0.535**	0.312**	0.171	1	0.296**	0.446**	0.297**
	Sig. (2-tailed)	0.000	0.001	0.072		0.002	0.000	0.002
	N	106	106	106	106	106	106	106
External information	Pearson correlation	0.291**	0.464**	0.166	0.296**	1	0.458**	0.183
	Sig. (2-tailed)	0.002	0.000	0.079	0.002		0.000	0.060
	N	106	106	106	106	106	106	106
Customer focus	Pearson correlation	0.614**	0.528**	0.286**	0.446**	0.458**	1	0.260**
	Sig. (2-tailed)	0.000	0.000	0.003	0.000	0.000		0.007
	N	106	106	106	108	106	106	106
Trust	Pearson correlation	0.318**	0.003	0.020	0.297**	0.183	0.260**	1
	Sig. (2-tailed)	0.001	0.973	0.836	0.002	0.060	0.007
	N	106	106	106	108	106	106	106

Note(s): **Correlation is significant at the 0.01 level (2-tailed)

Source(s): Author’s own creation

Table A5

Mean values showing differences in the factors structure for tenants and landlords

Factors		Mean	Std. Deviation	N
Regional and industrial growth	Tenants	4.26	1.29	63
	Landlords	4.70	0.79	43
	Total	4.44	1.13	106
Rent and vacancies	Tenants	4.84	0.94	63
	Landlords	4.70	0.66	43
	Total	4.78	0.84	106
GDP growth	Tenants	3.35	1.42	63
	Landlords	3.64	1.27	43
	Total	3.47	1.36	106
e-commerce	Tenants	3.45	1.57	63
	Landlords	4.14	1.19	43
	Total	3.73	1.46	106
External information	Tenants	4.19	1.11	63
	Landlords	3.99	0.78	43
	Total	4.11	0.99	106
Customer focus	Tenants	4.51	1.20	63
	Landlords	4.92	0.53	43
	Total	4.68	1.00	106
Trust	Tenants	4.13	1.24	63
	Landlords	4.59	0.69	43
	Total	4.32	1.07	106

Source(s): Author’s own creation

Table A6

Independent t-test showing statistical differences between landlords and retailers

		t-test for equality of means
	t	Sig. (2-Tailed)	Mean difference	Std. Error difference	Interval of the
					Lower	Upper
Regional growth	2.22	0.03	0.49	0.22	0.05	0.92
Rent and vacancies	−0.60	0.55	−0.10	0.16	−0.42	0.23
GDP growth	1.05	0.30	0.28	0.26	−0.25	0.80
e-commerce	2.66	0.01	0.72	0.27	0.18	1.26
External information	−0.56	0.58	−0.11	0.19	−0.49	0.28
Customer focus	2.16	0.03	0.44	0.20	0.04	0.84
Trust	2.28	0.02	0.47	0.20	0.06	0.87

Source(s): Author’s own creation

Table A7

Independent samples test of education level (gymnasium vs 3- to 4-year university degree)

	t-test for equality of means
	t	df	Sig. (2-Tailed)	Mean difference	Std. Error difference	95% confidence interval of the difference
						Lower	Upper
Regional growth	−1.43	76	0.156	−0.41536	0.28998	−0.99291	0.16219
Rent and vacancies	−1.82	76	0.073	−0.34029	0.18691	−0.71255	0.03198
GDP growth	0.67	76	0.507	0.22714	0.34083	−0.45167	0.90596
e-commerce	−1.97	76	0.052	−0.68500	0.34701	−1.37613	0.00613
External information	−0.15	76	0.885	−0.03571	0.24529	−0.52426	0.45283
Customer focus	−1.97	76	0.053	−0.49381	0.25090	−0.99352	0.00590
Trust	0.70	76	0.484	0.18619	0.26465	−0.34091	0.71329

Source(s): Author’s own creation

Table A8

Independent samples test of education level (gymnasium vs 5-year university degree)

	t-test for equality of means
	t	df	Sig. (2-Tailed)	Mean difference	Std. Error difference	95% confidence interval of the difference
						Lower	Upper
Regional growth	−0.66	57.00	0.51	−0.19	0.29	−0.78	0.39
Rent and vacancies	−2.68	57.00	0.01	−0.64	0.24	−1.12	−0.16
GDP growth	0.36	57.00	0.72	0.12	0.34	−0.56	0.80
e-commerce	−2.57	59.00	0.01	−0.94	0.36	−1.66	−0.21
External information	−1.29	57.00	0.20	−0.35	0.27	−0.89	0.19
Customer focus	−2.50	56.00	0.02	−0.74	0.30	−1.33	−0.15
Trust	−0.02	56.00	0.98	−0.01	0.31	−0.63	0.61

Source(s): Author’s own creation

Table A9

Independent samples test of education level (3–4 year education vs 5-year or higher university degree)

	t-test for equality of means
	t	df	Sig. (2-Tailed)	Mean difference	Std. Error difference	95% confidence interval of the difference
						Lower	Upper
Regional growth	0.56	0.58	0.14	0.25	−0.35	0.63	0.63
Rent and vacancies	−2.18	0.03	−0.37	0.17	−0.72	−0.03	−0.03
GDP growth	−0.33	0.75	−0.10	0.31	−0.73	0.52	0.52
e-commerce	−1.09	0.28	−0.34	0.31	−0.96	0.28	0.28
External information	−2.08	0.04	−0.44	0.21	−0.86	−0.02	−0.02
Customer focus	−1.22	0.23	−0.25	0.20	−0.65	0.15	0.15
Trust	−0.92	0.36	−0.19	0.21	−0.61	0.23	0.23

Source(s): Author’s own creation

Table A10

Mean values landlords and tenants education degree

Role in the negotiation process			Mean	Std. Deviation	N
Regional growth	Landlords	Gymnasium	4.53	0.62	8
		3- to 4-year university degree	4.99	0.80	19
		5-year or higher university degree	4.44	0.78	16
	Tenants	Gymnasium	4.03	1.53	20
		3- to 4-year university degree	4.33	1.31	30
		5-year or higher university degree	4.22	0.82	9
Rent and Vacancies	Landlords	Gymnasium	4.25	0.66	8
		3- to 4-year university degree	4.79	0.47	19
		5-year or higher university degree	4.83	0.79	16
	Tenants	Gymnasium	4.51	1.10	20
		3- to 4-year university degree	4.77	0.77	30
		5-year or higher university degree	5.58	0.73	9
GDP growth	Landlords	Gymnasium	3.63	1.33	8
		3 to 4-year university degree	3.74	1.38	19
		5-year or higher university degree	3.53	1.18	16
	Tenants	Gymnasium	3.60	1.56	20
		3 to 4-year university degree	3.13	1.45	30
		5-year or higher university degree	3.22	1.06	9
e-commerce	Landlords	Gymnasium	4.13	1.36	8
		3 to 4-year university degree	4.11	1.39	19
		5-year or higher university degree	4.19	0.89	16
	Tenants	Gymnasium	2.73	1.49	20
		3 to 4-year university degree	3.65	1.44	30
		5-year or higher university degree	3.94	1.91	9
External information	Landlords	Gymnasium	3.79	1.02	8
		3 to 4-year university degree	3.95	0.83	19
		5-year or higher university degree	4.15	0.58	16
	Tenants	Gymnasium	4.03	1.27	20
		3 to 4-year university degree	4.01	1.03	30
		5-year or higher university degree	4.78	1.01	9
Customer focus	Landlords	Gymnasium	4.67	0.47	8
		3 to 4-year university degree	4.96	0.55	19
		5-year or higher university degree	5.00	0.53	16
	Tenants	Gymnasium	4.05	1.54	20
		3 to 4-year university degree	4.58	1.01	30
		5-year or higher university degree	5.11	0.83	9
Trust	Landlords	Gymnasium	4.58	0.89	8
		3 to 4-year university degree	4.47	0.69	19
		5-year or higher university degree	4.73	0.59	16
	Tenants	Gymnasium	4.32	1.59	20
		3 to 4-year university degree	4.01	1.01	30
		5-year or higher university degree	3.78	1.18	9

Source(s): Author’s own creation