Towards a more efficient use of office-‐‑space
An empirical study investigating the effect of management related decisions regarding office usage on the space per worker ratio in the Dutch office market and which factors influence the need for office space in the future.
University: University of Amsterdam Amsterdam Business School Track: MSc Finance, Real Estate + Finance
Master Thesis
Name: Jan van Arkel Student number: 11397675 Date: 01-‐‑07-‐‑2017
Statement of originality
This document is written by student Jan van Arkel who declares to take full responsibility for the contents of this
document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
This paper investigates the effect of management related decisions regarding office usage on the space per worker ratio in the Dutch office market and which factors influence the need for office space in the future. To investigate this three research questions have been formulated; ‘Which management decisions regarding office usage lead to a reduction in space per office worker in the Netherlands?’, ‘What are the determining factors influencing the need for office space for an organization in the future in the Netherlands?’, ‘Does the effect of the firm’s size on the firm’s efficiency level hold for different size levels?’. To gather data regarding the previously stated research questions a survey has been held by the tenants of various real estate investment firms. Subsequently, this data is analyzed by an OLS regression model. Regarding to the research questions we can conclude that if we first look at the management decisions regarding office usage our models suggest with great significance that the implementation of desk sharing will result in a more efficient use of space, reducing the space-‐‑worker ratio with 1,01% per percentile increase of the shared desks. Regarding to the determining factors influencing the need for office space for an organization in the future there are a view; firstly, the location, the more expensive the office location the more efficient the use of space. Regarding to the firm’s size our models suggest that the bigger the firm the more efficient the space-‐‑worker ratio, this is in terms of employees but also in terms of leased space. Our research explains this scale advantage to happen when firms exceed the 1.000m2 of leased space, apparently for bigger firms it’s easier and more beneficial to implement new working techniques like desk sharing.
Table of Content
.
1. Introduction 6.
2. Literature review 8.
2.1. Drivers of change in space 8.
2.1.1. Sector 8.
2.1.2. Location 9.
2.1.3. Size 9.
2.1.4. Turnover 9.
2.1.5. Length of the lease 9.
2.1.6. Tenure 10.
2.1.7. New office techniques 10.
2.2. The Dutch office market 11.
3. Methodology 12. 3.1. Research question 1 13. 3.2. Research question 2 15. 3.3. Research question 3 17. 3.4. Survey Questions 17. 4. Data 19. 4.1. Descriptive statistics 19. 4.1.1. Independent variables 19. 4.1.2. Dependent variable 22.
4.2 Correlation of variables 25.
5. Results 26.
5.1. Regression model 1 26.
5.2. Regression model 2 27.
5.3. Regression model 3 29.
5.4. Results in perspective of literature 30. 5.5. Results in an economic perspective 32
6. Validation 34.
6.1. OLS assumptions 34.
6.2. Robustness checks 37.
7. Conclusion 40.
7.1 Limitations and further research 41.
8. References 43.
1. Introduction
One of the biggest challenges in the twenty-‐‑first century is going to be how to tackle climate change and how to reduce greenhouse gas emissions (United Nations, 2007). Investigation proves that buildings are estimated for almost half of all annual energy and greenhouse emissions and therefore a big step towards a more sustainable society is taken by ensuring that the construction, design and maintenance of the real estate sector is environmentally sustainable This sustainability is already becoming standard in the commercial office market for the owners and developers, the larger private and also the government tenants won’t even consider non-‐‑sustainable office buildings and therefore these ‘brown-‐‑buildings’ trade with a discount (Jones Lang LaSalle, 2016).
A sustainable building is also referred to as a ‘green building’, meaning: “The building uses a careful integrated design and strategy that minimizes energy use, maximizes daylight, has a high degree of indoor air quality and thermal comfort, conserves water, reuses material and uses materials with recycled content, minimizes site disruption, and generally provides a high degree of occupant comfort”. (Miller, 2008). This means that, next to the used materials, also the strategy within the building to minimize energy use contributes to a more sustainable building. In a Cushman and Wakefield report of 2010, which was aimed at corporate real estate executives, they suggested that the space per worker can be reduced up to 25 per cent, not just by reducing the current office space per worker but simply by increasing the number of employees per workplace due to implementing a policy of shared office space (C&W, 2010). By implementing such a policy, the energy use per worker will decrease and therefore ultimately less buildings are needed and the pollution per worker will decrease, having a huge contribution into making a building more sustainable and achieving the goals set by the Paris climate agreement (UNFCCC, 2016). Currently developers are already working on buildings which can interact with their tenants by mobile phone applications, making them communicate when, with whom and what kind of working space they will need during the day which enables the ‘building’ to optimally position the workers across the office space and therefore drastically reduces the space needed. Imagine what kind of effect an overall implementation of these techniques will have on the office stock and the demand for office space, not even mentioned the effect on the energy and greenhouse emission of the whole sector.
Because of the possible effect this reduction of space per worker could have on the real estate sector and on the reduction of greenhouse emissions in the world, this research is going to investigate which office management related changes lead to a reduction in space per office worker in the Netherlands and what are the determining factors influencing the need for office space for an organization in the future. Over the years’ various researchers have investigated these influences concluding that there are huge differences in-‐‑between countries and even between cities within those countries. For instance, in the Netherlands the latest specified studies have already been outdated, mainly due to technological improvements and a wider implementation of shared office space in the market. In this research, we’re going to investigate to what extent this change of office management has already been implemented, how much there is still to gain and what effect this will have on the Dutch office market in the future.
The Dutch office market hugely differs from other international office markets and is unique in its kind. In the Netherlands, the government is responsible for approximately 21% of the total leasing activity, to put this number in perspective note that the U.S. government is responsible for 11% of the leasing activity (C&W, 2016). Because governments are public organizations and therefore assumed to be less incentivized to cut costs we expect a huge
potential efficiency gain to be achieved in this sector. The Dutch office market, in international perspective, also differs in their policies regarding spatial planning, were the Dutch real estate market is one of the most regulated in Europe (Healey, 2004). When there is such a strict spatial planning policy it’s harder to diverge from this framework a simply build new office locations, therefore the existing space should be used more wisely and probably more efficient.
The second part of this research is going to focus on the effect of the implementation of new-‐‑office techniques for different size levels of firms. Assumable, these techniques are only beneficial for the bigger firms because of scale effects and due to a threshold level after which the possible negative external effects can be rejected. Therefore, we’re going to investigate if this assumption indeed can be observed in the Dutch office market and take these findings into account when looking at our prognosis for the future. Regarding to the existing literature all studies about the Dutch office market, from researchers but mainly from market parties, focus on the amount of space per worker that is currently being used but don’t further elaborate and investigate the specific drivers of this trend fully. Latest published research investigating this trend empirically in the Netherlands dates from 1996, although in this research they didn’t include new-‐‑ office techniques as a driver, probably because they weren’t implemented that widely. As to later investigations, there are a lot of researchers writing about the implementation of new-‐‑office techniques, what they are and the positive and negative effects. However, in the Netherlands, this pure 1 on 1 relation hasn’t been proved with an empirical study. The previous considering, this study is going to be the first empirical study in the Netherlands which is going to investigate the effect of the implementation of new office techniques like desk sharing and agile working on the firm’s efficiency level.
To investigate which management decision regarding office usage lead to a reduction in space per office worker in the Netherlands and what are the determining factors influencing the need for office space for an organization in the future, data will be gathered regarding the variables affecting the space per worker ratio. To gather this data a survey will be held by the tenants of various Dutch real estate investments firms located in the bigger cities across the Netherlands. After gathering this data an OLS regression will be used with all these firm-‐‑specific variables on the dependent variable ‘current office space per worker’. This dependent variable is generated by dividing each firm’s leased office space (in m2) by their number of office employees. Three regression models will be constructed, the first regarding the office management related changes that lead to a change in space per office worker, the second regarding to all the factors influencing the need for office space for an organization, and finally the last regression model will investigate whether there is a difference in the efficiency-‐‑levels in-‐‑between different size levels of firms.
The paper is structured as followed. Chapter two will discuss the previous literature written about the topic of this research. In chapter three, hypotheses are stated and the methodology of this research is described. The next chapter will further describe the data that is used to examine the research questions. Chapter five will display the results of this study and chapter six will do validity checks to examine the robustness of the results. Finally, chapter 7 contains the conclusion and recommendations for further research.
2. Literature review
2.1 Drivers of change in space
The previous years a lot has been written about these new workplace trends in office space and the effect this will have for the future demand for office space. These new workplace trends are something from the last decade because they are mainly a result of the increased mobility of the worker due to the implementation of new technology like the internet and the mobile phone (Harris, 2015). The earlier research mainly focusses on the drivers of change in space in the office market. One of these papers, written by J. Hakfoort and R. Lie in 1996, investigated four European office markets; Amsterdam, Brussels, Frankfurt and London, for company related factors influencing the office space per worker. The main drivers for this space are assumed to be the type of office, location, sector, company size, turnover, length of the lease, type of tenure, implementing space use strategies and the office layout. By conducting a survey by tenants in these markets they proved that office space per worker differs significantly per office-‐‑using sector. With a high space per worker ratio in the manufacturing, communications, transport and the utilities sector, and on the other hand a lower than average ratio in the business services and insurance sector. This paper also proved that office space per worker is higher in smaller buildings and that office space per worker tends to decrease when the location becomes more expensive. After this paper, more research has been done towards factors that influence the space per worker ratio. Most research regarding this topic is a literature study, meaning that the quantitative research regarding this matter is limited. This lack of quantitative research, especially in the Netherlands, also demonstrates the added value of this thesis. To give a clearer overview of these findings I will discuss the written literature per driver of change in space, starting with the office sector.
2.1.1 Sector
Like mentioned before J. Hakfoort and R. Lie showed in their 1996 paper that a higher than average space per worker ratio was found in the group consisting of transporting, communications, manufacturing and the utilities sector, on the other hand a lower than average space per worker ratio was found in the business and insurance sector. This can be explained by the different location preferences of these sectors, where companies in the business and insurance sector are more present at Central Business District locations. In a 2003 research done by Warren in both the Australian and the UK office market he found that administrative offices are the least densely occupied and call centers are the lowest in space per worker, which is the result of the different workplace designs where call-‐‑centers in general don’t need much extra office space. Another Survey done by M. West and G. Eve find evidence that the business and professional sector are lowest in their office density, which is in line with the findings of Hakfoort in 1996. West (2001) also find that the density in the public sector is also quite low due to new policy, this is in contrast with the findings of Warren (2003) who found that public servants in Australia utilize nearly 17% more office space than the average of the private sector. A similar investigation in the US by the General Services Administration (2011) found no significant difference between the governmental and private sector, both averaging around the 17.5m2 per worker. In the last years, many governments are implementing a more efficient use of office space per worker policy to lower their carbon footprint. This more strategic use of space was a high priority for the President Obama administration, which can be seen in a passage of the Telework Enhancement Act of 2010 were the President and Congress encouraged Federal agencies to further expand their use of teleworking and desk sharing to reduce their real estate footprint and their real estate costs. (General Service Administration, 2011). Also in the Netherlands, new goals for governmental agencies are being set with a reduction of the number of workplaces per employee from 0.9 to 0.7 (Rijksvastgoedbedrijf, 2015).
2.1.2 Location
Hakfoort and Lie (1996) also found prove in their research that office space per worker tends to be low in very expensive locations. This is also found by Warren (2003), he found significant prove that Central Business District offices have greater densities, followed by fringe locations and finally the suburban areas. West (2001) also found in his research that fringe and suburban locations are the lowest in density. Assuming, that the Central Business District locations are more expensive than fringe and suburban areas these findings are all in line and therefore the literature concludes that there is a negative relationship between the space per worker and the rent level because more central locations (Central Business Districts) are in general more expensive compared to more rural areas.
2.1.3 Size
Miller (2014) found in his research that the larger tenants are the ones focusing on using space more efficient, this group consisting of 2% of the tenants in the USA use in total 30% of the office space. This was also found in the Warren (2003) paper, here he found by survey that the densest organizations are the ones with leases bigger than 5.000 m2, he also found that comparable numbers in density were found by organizations with leases smaller than 250 m2. Because of scale advantages this increase in efficiency is expected for the bigger companies, for smaller companies this increase in efficiency is most likely explained by the total share of the company’s revenue spent on housing, which is higher for small companies and thus gives an incentive to reduce these costs. The highest amount of space per worker was found at organizations with 10-‐‑50 employees Finally, by a UK survey West (2001) also found that it is the larger organizations with the more optimal use of space with the highest density at companies with more than 200 employees and the lowest at the group with 1-‐‑9 employees. They concluded that it was because of the larger the company the bigger will be the total benefit of adopting new working practices. Also, regarding to the number of employees Miller (2014) found significant prove that the longer it takes for a firm to find new talent, the greater will be the used space per worker.
2.1.4 Turnover
Regarding to the company’s turnover one would expect that the lower this turnover is the more efficient will be the use of office space because of savings in costs. This was also found by West (2001) in their UK survey, they concluded that that the highest density in office use per worker was found at companies with a turnover lower than €3 mln, on the other hand the lowest density they found at companies with a turnover bigger than €25 mln. Quite contrasting results were found by Warren (2003) in his Australia survey, he concluded that the highest use in space was among organizations with a turnover below €500.000.
2.1.5 Length of the lease
Hakfoort (1996) concluded in his research that the length of the lease period is assumed to be one of the most important factors in determining the amount of office space per worker. This was based on economic theory because their survey done at companies in Brussel, Amsterdam, Frankfurt and Paris, couldn’t prove this to be significant. They assumed that a company’s growth rate is also imbedded in the amount of space rented, therefore the longer the lease the more space a company will rent up front if they expect to grow in terms of employees. This theory was proved with significant results by Warren (2003), he found by survey in the Australian office market that companies with a 6-‐‑10-‐‑year lease used the office space densest in contradiction to the lowest density at the 20-‐‑50-‐‑year contracts. Also, West (2001) proved this to be true in the UK office market with the lowest amount of space per worker at leases shorter than 5 years and the highest amount of space at leases longer than 10 years. Quite interestingly this theory was disproved by Miller (2014), he found
that longer-‐‑term leases do not result in a higher number of space per worker up-‐‑front because of a shift in the office market were shorter leases became more common over time, also there was a wider use of expansion clauses within the contract allowing the tenant an option to rent extra office space when needed.
2.1.6 Tenure
Regarding to the two West (2001) research they proved with significant results that there is a denser use of space per worker when the office is leased in comparison to when owned in the UK office market, this was also found by Warren (2003) in his research done in the Australian office market. They both conclude that this is because an office space leased is in general more expensive compared to all the costs associated with a mortgage. This theory is in line with Hakfoort (1996), Ramidus (2013), both West (2001) and Warren’s (2003) findings that more expensive office locations in general have a more efficient use of space per worker associated with the higher costs.
2.1.7 New office techniques
The most interesting and probably the most significant effect on the used office space per worker is the implementation of new office techniques by the management. In Gibson’s (2003) research towards flexible working he found that there are three categories associated with the terminology of flexible working, these are: flexible contracts, flexible time and flexible locations. This last flexibility has led to the expansion of new working practices, these practices entail that workers are no longer tied to a single place of work, but instead should seek to work at the best environment or place for the task (Gibson, 2003). Kim (2016) showed that the office space per worker has declined with almost 50% over the last two decades, only assumed to decrease even further with a wider implementation of new office techniques. Many terms have evolved regarding to these techniques like: ‘agile working’, ‘flexible working’, ‘activity based working’ and ‘smart working’, but they all have in common that they’re evolved from an increased worker mobility, which includes working from other locations next to the traditional office but also the sharing of workplaces and desks at the office (Harris, 2015). This trend in the market regarding the flexible workplace can be seen by the upcoming of the serviced office sector, this sector focusses on short-‐‑term leases of small areas of office space which provide the tenant with a maximum of flexibility in their lease. This sector has quadrupled in the UK in the last two years, currently they cover up to 3% of the total office market but this is expected to increase till 10% in the upcoming years (Ramidus, 2014).
By implementing these new office techniques research shows a huge decline in the average space per worker, Miller (2014) finds in his research that in the US the average office space is currently at 23m2 per worker but by using space strategies this number can decrease till 11m2 for at least 36 percent of the firms of his sample, this will result in a reduction of their carbon footprint by 50% (Miller, 2014). In 2011, the General Services Administration of the US Government encouraged Federal Agencies to implement a wider use of telework to reduce their carbon footprint and real estate costs after they achieved a 9m2 per worker average in the Washington building only by using shared workplaces and telecommuting (GSA, 2011). The effect of the implementation of new office techniques on the space per worker ratio has also been investigated by Miller (2014), Warren (2003) and West (2001), they found in their research by survey in the US, UK and Australian office market the implementation of these techniques to be highly significant. Harris (2015) introduced in his paper the concept of ‘spaceless growth’, indicating firms being able to grow their workforce within their current amount of space by implementing new office techniques and thus shift towards higher densities and higher utilization rates. Halvitigala and Reed (2015) found in their UK research that simply implementing a policy of desk sharing would result in a decrease of office space of 20%.
The implementation of new office techniques also comes with a downside widely covered by the literature. Kim (2016), Halvitigala (2015), Appel-‐‑Meulenbroek (2010) and West (2001) addressed the obstacles of day-‐‑to-‐‑day variations of employees, the loss in productivity due to the finding and setting-‐‑up of desks, the lack of comfort and the inability to personalize a workspace. They concluded that if these obstacles aren’t addressed in the right way they may have an overall negative impact on the productivity of the worker and thus cross out the gains made on the more efficient use of office space. Ramidus (2013) showed in his study of the London office market that the effective density of a workplace rarely exceeds the 80% because it then will affect the worker’s productivity, he concludes that therefore the currently observed decrease of space per worker will level out at some point. De Been (2015) point out that when implementing combo-‐‑ or flex offices extra attention should be considered for the facilitation of places with extra privacy where workers can concentrate, if not this will negatively affect the worker satisfaction.
2.2 The Dutch office market
In the current research a huge difference in the space per worker can be observed across different countries. The General Services Administration (2011) of the US government found in their research that the average space per worker in the USA was 17.6m2 for private-‐‑ and 17.9m2 for public companies. In an occupier density study done by Ramidus (2013) he found that the mean density of London in 2012 was 10.8m2, which decreased with 1m2 per worker compared to 2008. A decrease of office space per worker in London can be observed because Warren (2003) found this density being 16.3m2 in 2003. In the same research, Warren found by survey that the density in Australia at the same moment in time was almost 25% higher (20.6m2). All indicating different densities across the world. Regarding to the Dutch office market, the Dutch government is explicitly targeting their agencies office use at 11 m2 per worker, in the late 90’s this number was around the 20 m2, which is almost a 50% reduction in their office use in the last decades (Rijksvastgoedbedrijf, 2015). On the other hand, in the US, this target space per worker, set by the government for their agencies, is set much higher at 20 m2 (GSA, 2011). Van Meel (2000) found that the Dutch office design differs from other countries, were most of Dutch offices are low or mid-‐‑rese buildings, the floor depth is defined and the larger part of the workstations are in vicinity of a window. Since 1990 a trend is observable of a change from a more cellular structure towards combi-‐‑ and flex offices, this probably partly clarifies the observed reduction in office space per worker of the last decades in the Dutch office market. The International office markets show huge mutual differences due to different policies regarding spatial planning were the Dutch real estate market is one of the most regulated in Europe (Healey, 2004). When space is more regulated it’s harder to simply expand en therefore the existing space should be used more wisely. Also, in the Netherlands the government is responsible for approximately 21% of the total leasing activity. Usually governments are in general more generous to their workers and less incentivized to cut costs, because in the U.S. the government is responsible for 11% of the leasing activity a difference in these markets is assumed (C&W, 2016).
Al the previously described variables are the drives of space which will assumingly influence the company’s space-‐‑worker ratio. In this research, we’re going to investigate which of these variables significantly influence the space-‐‑worker ratio, at first, we’re going to add all the variables to the model and subsequently we will drop or add variables in our search to the most explaining model. Note that only, regarding to the variables in the final model. we can analyze the variables which are in the final regression model. The explanatory power of the final model and the variables included is totally
dependent on the dataset, which we’ll construct by survey. Unfortunately, we can’t analyze the variables not included in the final regression model.
3. Methodology
The research question of this thesis is going to be: “Which office management decisions regarding office use lead to a reduction in space per office worker in the Netherlands and what are the determining factors influencing the need for office space for an organization in the future”. Before being able to investigate this research question data should be gathered regarding the variables affecting the space per worker ratio. To gather this data a survey will be held by the tenants of the Chalet Group, this is a Dutch real estate investment fund with offices evenly spread around the bigger cities in the Netherlands. The data gathered by survey is regarding:
-‐‑ Years in business -‐‑ Rent
-‐‑ Number of employees -‐‑ Length of the lease -‐‑ Company sector -‐‑ Space expansion option -‐‑ Expected revenue -‐‑ Policy of desk sharing -‐‑ Growth rate -‐‑ Amount of desk sharing -‐‑ Time to fill a vacant position -‐‑ Management hierarchy -‐‑ Location -‐‑ Utilization rate -‐‑ Size of office location -‐‑ Time out of the office -‐‑ Policy of teleworking -‐‑ Rent increase
After gathering this data an OLS regression will be constructed with a selection of the previous independent variables on the dependent variable ‘current office space per worker’. This dependent variable is generated by dividing each firm’s leased office space (in m2) by their number of office employees.
𝑂𝑓𝑓𝑖𝑐𝑒 𝑠𝑝𝑎𝑐𝑒 𝑝𝑒𝑟 𝑤𝑜𝑟𝑘𝑒𝑟 = 𝑂𝑓𝑓𝑖𝑐𝑒 𝑠𝑝𝑎𝑐𝑒 𝑙𝑒𝑎𝑠𝑒𝑑 (𝑚 ~) 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠 𝑜𝑓𝑓𝑖𝑐𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛
By doing this it will be observable which of the previous independent variables correlate to what extent and which implications this will have for the research question. The research question, like stated before, can be divided into three sub questions.
Question 1:
Which management decisions regarding office usage lead to a reduction in space per office worker in the Netherlands?
Question 2:
What are the determining factors influencing the need for office space for an organization in the future in the Netherlands?
Assumed there is a significant effect of the firm’s size, measured in the amount (m2) of office space, on the firm’s efficiency level, we’re also going to investigate the following research question:
Question 3:
The difference between the first and the second regression model is that the first one is regarding the management decisions which could lead to a reduction in the space per office worker. In this model, only the variables which are changeable by management are included. Think of causes like the office location, different working policies and certain clauses in the rental contract. In the second model, all the relevant variables are included to see which factors indeed do significantly affect the firm’s efficiency level, therefore the determining factors can be observed whereby firms can detect how they can reduce their space per worker ratio in the future. With these factors and looking at the current specifics of the Dutch office market on the other hand a prognosis can be made about the Dutch office market.
3.1 Research question 1
Regarding to the first research question; “Which management decisions regarding office usage lead to a reduction in space per office worker in the Netherlands?”, the following independent variables will be included in the OLS regression model:
-‐‑ Space expansion option (Sei) -‐‑ Policy of teleworking (Pti) -‐‑ Office location (Li) -‐‑ Time out of the office (Tti) -‐‑ Management hierarchy (Smi) -‐‑ Amount of desk sharing (Asi) -‐‑ Policy of desk sharing (Psi)
Space expansion option (Sei):
The variable ‘space expansion option’ will be included in the regression model as a dummy variable, valuing 1 or 0 whether an option for space expansion is included in the current lease. The hypothesis regarding this independent variable is that an option for space expansion will allow companies to lease their space more efficient and thus the space per office worker will be lower.
Office Location (Li):
The variable ‘office location’ will be included in the regression model as a 6-‐‑variable dummy for the different office locations: Central Business District (CBD), Urban, Suburban, Business park, Industrial, Other. The hypothesis regarding these different office locations is that the more central, thus the more expensive, locations will show a lower amount in space per office worker. Regarding to William Alonso’s monocentric city model the most central and expensive office location is the Central Business District (CBD) followed by urban, business park, suburban, industrial and rural locations (Geltner, 2013) .
Fig 1. Monocentric city model
Management Hierarchy (Smi):
Management hierarchy is measured by the amount of specialized office spaces solely dedicated for management, including personal offices and desks. This independent variable is measured in a percentage of the total office space. The hypothesis is that the higher the number of dedicated office space the less space can be used as intensively and this will lead to a higher amount of space per worker, this hypothesis is in line with the literature (West & Eve, 2001).
Policy of desk sharing (Psi):
The variable ‘policy of desk sharing’ will be included in the regression model as a dummy variable, valuing 1 or 0 whether a policy of desk sharing is being employed. The hypothesis regarding this independent variable is that a policy of desk sharing will allow companies to use their office space more efficient and thus the space per office worker will be lower.
Policy of teleworking (Pti):
The variable ‘policy of teleworking will be included in the regression model as a dummy variable, valuing 1 or 0 whether a policy of teleworking is being employed. A policy of teleworking enables employees to work from distant locations due to a remote connectivity with the office. The hypothesis regarding this independent variable is that a policy of teleworking will result in a lower amount of space per office worker.
Time out of the office (Tti):
When a policy of teleworking is being employed the variable ‘Time out of the office’ is a continuous variable and measures how many hours a day the average employee works from a distant location. The hypothesis regarding this independent variable is that the more hours a day are spent outside the office the lower will be the amount of space per worker.
Desk sharing (Asi):
When a policy of desk sharing is being employed the variable ‘Desk sharing’ measures what percentage of all the workplaces are non-‐‑dedicated spaces and suitable for desk sharing. The hypothesis regarding desk sharing is that the bigger the amount of non-‐‑dedicated space the lower will be the amount of space per office worker.
Office space per worker (SPW1):
The dependent variable used in this regression will be the office space per worker (SPW1). This variable is generated by dividing each firm’s leased office space (in m2) by their number of office employees.
The OLS regression model with the previous independent variables on the dependent variable ‘Office space per worker’ (SPW1) is as follows:
SPW1 = a + b1 * Sei + b2 * Li + b3 * Smi + b4 * Psi + b5 * Pti + b6 * Tti + b7 * Asi + e
The main hypothesis is that the possibility of desk-‐‑sharing and working at home has the biggest impact on the reduction of office space per employee. This is in line with the different papers who studied this effect; Miller (2014), Harris (2015), Kim (2016).
3.2 Research question 2
Regarding to the second research question; “What are the determining factors influencing the need for office space for an organization in the future in the Netherlands?”, the following independent variables will be included in the OLS regression model:
-‐‑ Years in business (Yi) -‐‑ Rent (Ri)
-‐‑ Number of employees (Ei) -‐‑ Length of the lease (Lli) -‐‑ Company sector (Cs) -‐‑ Space expansion option (Sei) -‐‑ Revenue per worker (Eri) -‐‑ Policy of desk sharing (Psi) -‐‑ Growth rate (Gi) -‐‑ Amount of desk sharing (Asi) -‐‑ Time to fill a vacant position (Ti) -‐‑ Management hierarchy (Smi) -‐‑ Location (Li) -‐‑ Utilization rate (Ui)
-‐‑ Size of office location (Si) -‐‑ Time out of the office (Tti) -‐‑ Policy of teleworking (Pti) -‐‑ Rent increase (Rii)
Years in business (Yi):
The variable ‘Years in business’ will be included in the regression as a continuous variable representing the amount of years the company is in business. The hypothesis is that the longer the company is in business the better do they understand their need for space and thus the lower the amount of space per worker.
Number of employees (Ei):
The number of employees will be included in the regression as a continuous variable. The hypothesis is that the bigger the firm in term of employees the more efficient will be their use of space due to scale advantages.
Company sector (Cs):
The variable ‘company sector’ will be included in the regression model as a 5-‐‑variable dummy for the different company sectors: Business, Industrial, Government, Non-‐‑profit, Other. The hypothesis is that the lowest amount of space per worker will be found in the business sector and the highest amount at the government.
Growth rate (Gi):
The company’s growth rate is measured in the number of workers the company anticipates to hire on average per year in the next 5-‐‑10 years, this is a continuous variable. The hypothesis is that the bigger the growth rate the lower will be the amount of space per worker.
Revenue per worker (Eri):
The company’s expected revenue per worker is a continuous variable and displays the company’s expected revenue in the current financial year divided by the number of workers. Expected is that the bigger this revenue per worker the bigger will be the office space per worker because there is less incentive to work more efficient.
Time to fill a vacant position (Ti):
The time to fill a vacant position is a continuous variable and shows the amount of time in months the company needs to hire a new employee. The hypothesis is that the more time is needed the greater will be the excess space thus the higher the amount of space per worker.
Size of office location (Si):
The size of the office location is a continuous variable and displays the amount of space the company uses for the office location in square meters (m2). The hypothesis is that the bigger the office location the lower will be the amount of space per worker due to scale advantages.
Rent (Ri), Rent increase (Rii):
The firm’s rent level (Ri) is measured in the percentage of the company’s revenue that is being spent on their housing needs. The hypothesis is that the lower this percentage the higher will be the amount of space per worker because there is less incentive to work more efficient. The variable rent increase (Rii) is a continuous variable and shows the amount of rental increase needed before the company would use space more efficient, the hypothesis is that the higher this number the higher will be their current space per worker.
Length of the lease (Lli):
The length of the lease is a continuous variable and shows the amount of years left on the company’s current lease, if the building is owned by the company this number is equal to the longest possible lease. The hypothesis is that longer leases will imply more space per worker because of expected future growth.
Utilization rate (Ui):
The utilization rate shows the percentage of a typical workday that a workplace is being used. The hypothesis is that the higher this utilization rate the lower will be the amount of space per worker.
