Rent Premiums and Vertical Sorting in Amsterdam’s Multi-Tenant Office Buildings

Over the past five decades urban economic theory has focused on analysing urban spatial structure based on the Alonso (1964), Mills (1967), Muth (1969) framework of monocentric cities. This framework concentrates on land prices’ impact on horizontal spatial structure and land use. Consequently, mainstream urban economics is characterised by an implicit assumption that treats cities as flat, with little or no consideration of the vertical spatial structure (see Duranton and Puga 2015 for a recent review). In economic geography and urban economics’ approach to productivity tall buildings constitute an important, density-increasing typology that fosters agglomeration. Additionally, building height is strongly influenced by competition for status among developers, investors and cities (Helsley and Strange 2008). Tall buildings can therefore be considered a strategic component of urban economic development and are predicated on a variety of factors namely economic cycles, local land use regulations, a city’s global positioning and the search for status/prestige that stakeholders aim to achieve through these developments (Barr 2012; Garza and Lizieri 2016). Notwithstanding their importance, empirical research on tall buildings, rents and vertical spatial structure is still embryonic.

There has been a surge of skyscraper development across the globe particularly in the last two decades as a result of major technological advancements. This, coupled with concentration of wealth in particular world regions, has led to a competition for building higher. The Council of Tall Buildings and Urban Habitat (CTBUH) reports that by the end of 2016 the number of buildings taller than 200 m amounted to 1168 globally (CTBUH 2017a). In the Netherlands, considering also local land use regulations, six out of ten buildings above 100 m and eight out of the ten tallest structures have been constructed in this millennium. The Dutch market is experiencing an increase in high-rise constructions, as there are currently 51 high-rise buildings proposed or under development (CTBUH 2017b). The above figures point to a renewed global skyscraper construction boom and a growing importance of tall buildings in the Netherlands, which calls for further in-depth research on the economic aspects underpinning skyscraper development.¹

This paper contributes to the growing body of knowledge on the economics of tall buildings by investigating the rent premiums associated with higher floor location and different industry sectors that occupy commercial office space. The study is part of an embryonic body of knowledge that analyses exact, within building location to estimate vertical rent gradients and assess tenants’ willingness to pay to locate on higher floors. To the best of our knowledge, it is the first that assesses height premiums after holding view constant. In their quest for height firms are driven by a search for status – ‘showing off’ by being on top of others; demand for view amenities that raise profit rather than directly reflect their utility function; signalling the quality of their product and other factors (Liu et al. 2018). Our empirical analysis shows that after holding view constant firms still pay significant premiums – which are roughly 70% of the initial amount – to be located on higher floors. These we mainly attribute to signalling and other, firm-level differences among office tenants.

The empirical analysis uses a dataset of 627 office transactions in thirty-three multi-tenant buildings of ten floors or taller rented during the period 2000–2016. It employs standard hedonic models to investigate rent premiums and various aspects of vertical sorting of different industries. We additionally use spatiotemporal models based on widely accepted approaches to spatial econometric analysis of real estate data (Pace et al. 2000; Tu et al. 2004; LeSage and Pace 2009; Nappi-Choulet and Maury 2009; Dubè and Legros 2014; Nase et al. 2016, among others). Spatial econometric literature indicates addressing estimation bias or inconsistency and improved model fit over OLS estimates as the typical rationale for this model choice, given that real estate data are of spatial character. Our empirical estimates point towards more plausible spatiotemporal estimates based on theoretical expectations for a limited number of variables. However, model performance was considerably lower compared to the OLS estimates with spatial (expert delineated submarket) fixed effects indicating the efficiency of submarket delineation in the Amsterdam office market. The remainder of this paper is organized as follows. The next section starts by analysing the theoretical underpinnings of the economics of tall buildings and subsequently focuses on rent premiums, vertical sorting and the willingness to pay for locating on higher floors. Section three provides an overview of the data and variables used in the analysis and section four describes the methodological approach. The fifth section provides a detailed account of the empirical findings in two parts. The first part focusses on the rent premiums for various industry sectors and different aspects of height (vertical location). The second part concentrates on the vertical sorting of different industries based on preferences for view and/or status. Section six draws conclusions to this study.

Tall buildings can be considered a way to signal economic strength for builders, developers, international corporations, government entities and cities, making building height an important strategic component in urban economics (Barr 2012). The average global building height has significantly increased during the past fifteen years. While the tallest 100 buildings in the world had an average height of 286 m in 2001, this has since risen to 362 m by the end of 2016 (CTBUH 2017a). These figures are indicative of the surge in high-rise buildings and point to the increasing importance of their role in urban development. The literature on skyscraper development is characterized by a small number of prevailing theoretical approaches summarized by Garza and Lizieri (2016). The key theories are namely the traditional microeconomic model, the game theoretic approach, the business cycle behaviour model and the global cities influence.

The traditional microeconomic theory is based on the monocentric city model, where competition for scarce land in the city centre among different sectors drives up land prices and subsequently determines the optimal building height and shows height increases as a function of economic activity within a city. One of the earliest studies on the economics of building height uses these principles to investigate profit maximisation based on costs and income flows for various height levels of a hypothetical building considering excessive height as a response to increasing land values (Clark and Kingston 1930). However, many contemporary cities are not characterized by the monocentric model and building height does not monotonically decrease with distance from the CBD but shows considerable variations (Duranton and Puga 2015). The waves of different building height within the CBD are mainly caused by the coexistence of multiple production centres with their own gravity centres and the endogenous relationship between land value and agglomeration (Barr 2012).

The game theoretic approach developed by Helsley and Strange (2008) focuses on the competition of developers in reaching ‘the tallest’ building heights. This contest explains overbuilding, as every game participant chooses building heights which exceed profit maximization heights and result in the tallest building being much higher in comparison to the surrounding buildings. Record breaking buildings show some cyclicality over time, exceeding each other in rapid succession within the 1920s, 1970s and 2000s. This model explains status, overbuilding and the dynamics within the skyscraper race concluding that excessive height is a result of the competition for status/power/ego. Ahlfeldt and McMillen (2018) investigate historical land prices and building height in Chicago and find evidence that excessively tall buildings are less likely to be constructed at the same location or in the same or subsequent decade than other tall buildings indicating support for the game theoretical model.

The work of Barr (2010, 2012, 2013) on the economic determinants of building height stands somehow at the intersection of these two theories. The author finds that skyscraper height is primarily determined by local and national economies, land use regulations and taxation (Barr 2010). Record breaking height and the quest for status are only driven by the right combination of ego and economics and occur only when the opportunity costs for both is relatively low. Subsequent research considered ego as an important factor for building height (Barr 2012). Results showed the search for status had increased building height by approximately 15 floors at the end of the twentieth century. Height competition increases significantly during times of economic growth, due to the lower opportunity costs for seeking social status. Additionally, economic factors and land use regulation were important determinants of building height. In a comparative study of building height determinants for the cities of Chicago and New York, Barr (2013) finds that general economic and policy variables are mainly responsible for the variation in height between the cities. However, each city responded differently demonstrating that local factors have an important influence on building height.

Theory on global cities has focused on economic and sociological aspects analysing capital flow, transaction volumes and communication networks to show that these areas are able to attract high skilled labour due to their concentration of advanced producer services. This has implications for the traditional economic model, as the economic size of the city alone is not a determining factor for building height but should also consider global connectivity and world city status. The business cycle approach is based on the Skyscraper Index which links record breaking tall buildings (considered as overinvestment or capital accumulation into bricks and mortar) to global economic downturns in 1929, 1974, 1998 and 2008 (Thornton 2005). This approach does not provide a causal relationship among the two phenomena and has found little application in academic research. More recently, Barr et al. (2015) showed that popular beliefs related to the use of the Skyscraper Index to predict global business cycles do not hold. The authors used cointegration analysis and Granger causality tests to conclude that GDP (a proxy for national income) Granger causes skyscraper height while there is no evidence of reverse causality among the two. This is more in line with rational economic behaviour of income (wage) capitalization into various real estate amenities (height in this case) as outlined by Roback (1982).

These theories depict skyscraper height as a function of income in general (demand side) and developer competition for status (supply side). Particularly the evidence based empirical work on the demand side seems to favour mainly the hypothesis that skyscraper height is generally an outcome of rational economic behaviour. According to these studies the non-economic drivers of height such as developer ego tend to happen only in boom times (Barr 2012; Barr et al. 2015). The demand side theories and related evidence hypothesize that excessively tall buildings can be only explained by a contest of developer’s egos since they are overbuilt (exceed optimal height) in the narrow economic sense (Helsley an Strange 2008; Ahlfeldt and McMillen 2018). Our research is positioned on the demand side theories of building height through its concerted analysis of explaining and estimating tenants’ willingness to pay for locating on higher floors, the possible existence of sorting along different floor levels and its causes across various industry sectors.

A theoretical framework for the vertical spatial structure and systematic sorting² of industry sectors based on the tension between vertical access costs and amenities has been proposed by Liu et al. (2018). It predicts that higher productivity, higher amenity-oriented industries tend to locate on higher floors. These predictions are supported by empirical evidence which also shows the existence of a non-monotonic vertical rent gradient. Throughout the paper the authors unveil three hypotheses in attempting to explain why firms are willing to pay more to be on higher floors. First, the upper floors offer better views which to commercial tenants are important only if they increase profits. In this context, the authors offer mixed relations to both increased productivity and reduced HR costs. Second, firms tend to locate on higher floors to demonstrate their (powerful) status by positioning themselves ‘above the others’. This behaviour is very similar to the ego-driven developers who build higher to dissipate competition. In the recent years of increased corporate social responsibility this hypothesis might not hold across the wide spectrum of industries. Third, by using height firms tend to signal quality of their product to customers. Consequently, a higher location is assumed to be worth more to high-productivity tenants.

In the light of the above evidence, our first goal it to test for the existence of vertical rent premiums and investigate their nature. Liu et al. (2018) show that vertical rents are independent of the within building and nearby employment and increase approximately 0.58% on average per floor although accessibility decreases with height. The sector persistently seen on higher floor levels was law firms. Within the Dutch context, Koster et al. (2014) found significant positive rents in taller buildings across four submarkets in the Netherlands including Amsterdam. Their results indicated 4% rent premiums on average for a 10-m building height increase which is attributed to building agglomeration economies due to the increase in productivity, landmark effects and panoramic views. Our second goal is to investigate rent differentials among different industry sector office tenants to test for the existence of productivity-related, higher wages’ capitalization into prices consistent with the hedonic price theoretical framework of Lancaster (1966) Rosen (1974) and Roback (1982).

Following from the first two goals, our third goal is to test the existence and explain the causes of vertical sorting. Liu et al. (2018) found evidence that sales per worker and employment in industries are positively correlated to floor number, indicating that the highest floors contain the most productive industries. Additionally, larger firms have established headquarters on higher floor levels. It is argued that the location of these ‘trophy’ tenants is based on strong amenity orientation and corresponding status. An interesting finding in this context relates to the association of within building relative location with ‘social power’ (Dorfman et al. 2017). The authors perform a series of experiments to conclude, among others, that information about people’s floor location signals their social power. We consider the concept of individual social power to be closely related to the concept of firm-level status described earlier.

As evidence points to three key attributes associated with higher floor location, in the remaining parts of this paper, we focus particularly on these aspects when explaining the causes of vertical sorting among different industry sector tenants. This research adds to the embryonic body of knowledge on the economics of height in two ways. First, it improves upon previous findings about the Dutch office market by providing floor level and industry sector rent premiums, factors which have not been previously measured due to lack of related information. Second, it provides the first evidence to date on the causes of vertical sorting through a concerted and laborious process of variable design and subsequent econometric analysis. These particular features of variable design and more general information about the data used in this study are explained in detail in the next section.

The database used in this research is constructed from a variety of sources. Their combination enables the analysis of commercial rent transaction values, the related industry sector and location within a building for each transaction, in addition to the common hedonic characteristics such as size, age etc. The market transaction data are sourced from Cushman & Wakefield (C&W). Their general market database, rental contracts and rent roll records of office building transactions provide information regarding rent prices, transaction date, size, building age, renovation date (when applicable), building height (in floors), number of parking spaces in a building, and the ratio Net/Gross area. The latter we use as a proxy for building design efficiency, particularly considering that in tall buildings competition among developers might lead to unused physical space mainly on the top levels (following Helsley and Strange 2008). Rent rolls and rental contracts furthermore specify the exact vertical location of tenants within the building. The variable list was further enriched with information from Vastgoeddata, an online commercial real estate data provider. The database combines several data sources from the Dutch Land Registry, Strabo, Ruimtelijke Plannen and Creditsafe. The Creditsafe’s data is used to identify and categorize the tenants according to their corresponding business classification code and industry sector. The industry sectors categorization follows that of the Dutch Chamber of Commerce, which employs the SIB 2008 code Standaard Bedrijfsindeling 2008 (in Dutch).

The tall commercial office buildings within this study are selected based on data acquired from the website www.​emporis.​com that provides technical information on tall structures. This dataset provides accurate information about high-rise structures across the globe and has been employed in previous studies of Manhattan and Chicago (Ahlfeldt and McMillen 2018; Barr 2012). The website adopts a context-based definition of tall buildings, and the threshold for Amsterdam stands at forty meters (ten floors on average). We use this threshold for our subsequent data collection whereby from the initial database we select only transactions registered on multi-tenant office buildings of ten floors or more. Additionally, we omit all entries classified as retail to obtain a final database of 627 transactions located in thirty-three buildings across Amsterdam transacted during the period 2000Q1 – 2016Q3 (Fig. 1). Building heights were verified with the 3D-GIS information on the cadastral system of the Netherlands (BAG - Basisregistraties Adressen en Gebouwen).

Table 1 provides an overview of the variables generated in the data collection process indicating, with the appropriate rationale, the ones which were not used in the empirical analysis. To ensure that the data is comparable across the analysis timespan the dependent variable (the only economic variable) is corrected for currency exchange rates and inflation. All rental contracts in guldens (Dutch currency before 2002) have been transformed to Euros using exchange rates from CBS (Dutch central bureau for statistics). Subsequently, all dependent variable values are corrected for inflation and reported in 2016 Euros. The average property in the database has a rental transaction price of €324/ m² /year with an area of 960 m², located in buildings approximately 20 floors tall that host on average 35 tenants. It was constructed or renovated approximately 12 years ago and has a EPC score of 1.08 which corresponds to energy label B. The relatively high number of tenants to number of floors may be attributed to the presence of the World Trade Centre Amsterdam transactions in the database.³ The Centre hosts some of the largest multi-tenant office towers in the Netherlands, with tenant numbers varying from 37 in tower D to 88 in tower A, and is located in the South Axis submarket (Fig. 1). This submarket is considered the top office location in the Netherlands with the highest transaction activity. Such a dominance in transaction volume is also reflected in the composition of our database as approximately 63% of the entries are registered in this submarket.

With regard to the variable groupings of interest, we assign a vertical location to each observation based on the highest floor on which it is situated (High Floor) to capture better the view amenity premium and status/power associated with height (Liu et al. 2018). Following evidence on ‘social power’ (Dorfman et al. 2017), we calculate a ‘Relative floor’ variable by dividing the floor on which a transaction is located by the total number of (above ground) floors in the respective building. This variable has a range from zero, for ground floor located offices, to one for offices located on top of their buildings (penthouses). We further isolate penthouse transactions in a variable that takes the value of one for all observations that have a ‘Relative floor’ value of 1 and zero otherwise. These two variables proxy for the prestige/status (or ‘social power’) aspect of height as they have been constructed disregarding view potential or height of surrounding buildings. However, as we will show in the following sections it is difficult to completely isolate/separate the two factors. Using the vertical location variable (High Floor) we additionally specify categories of five floors whereby more than half of the observations are located between floors one and ten. A further 25% are located between floors eleven and fifteen and only 10% of the transactions occupy floors higher than level twenty (Table 1).

In order to investigate view premiums for different industry sectors we construct a ‘view potential’ variable (View) based on the visible area from each floor level in all the thirty three buildings figuring in our transaction database. The necessary data in the form of shape files and geodatabases with feature classes is obtained from the cadastral system of the Netherlands (BAG database). This is in turn verified (and when necessary amended) with the 3D ArcGIS scenes provided by ESRI Netherlands, site inspections and Google Street view analysis. We emphasise here the ‘potential’ dimension of view and restrict our analysis only to its ‘quantitative’ aspect (visible area) due to the fact that the C&W database does not include information about the exact location of each transaction within a particular floor. In other words, we do not know which side of their building any given transaction is facing. This does not allow for the analysis of more qualitative aspects of view including price differentials for different view types namely, river/water feature, (historic) city scape, park etc. considering that the BAG 3D data enables such differentiation. There have been applications of such analysis in computer and built environment related research areas for descriptive purposes. However, to the best of our knowledge, this is the first paper to employ such a variable to estimate rent premiums for views. Appendix gives a detailed description of how this variable is constructed including screenshot examples of the Line of Sight 3D analyst tool in ArcScene.

The methodological approach of this paper is based on the Lancaster (1966), Rosen (1974), Roback (1982) hedonic price theory for the analysis of height and view related premiums and the understanding of different reasons for industries’ vertical sorting. Spatiotemporal modelling routines of real estate data are additionally used as described in Pace et al. (2000) and further extended to the office sector by Tu et al. (2004), Nappi-Choulet and Maury (2009) via Bayesian estimation and Chegut et al. (2015) via GMM estimation. We particularly exploit the properties of the spatiotemporal model specification that aid Maximum Likelihood computation and follow the estimation line of Pace et al. (2000).

The baseline hedonic model is given in matrix notation in Eq. 1 belowwhere, Y is the n × 1 vector of observations on the dependent variable, ι is an n × 1 vector of ones related to the constant α to be estimated. X is an n x k matrix of hedonic property characteristics (in this case including also spatial fixed effects – submarket dummies), β is a k × 1 vector of parameters to be estimated associated with these characteristics, D is a n x (t-1) matrix of time period dummies and δ is a (t-1) × 1 vector of time dummy parameters to be estimated.⁴ The n × 1 vector ε of error terms is assumed to be independent and identically distributed (iid) with mean zero and variance σ². In the presence of spatial autocorrelation this assumption is violated and OLS estimates become inefficient.

Spatiotemporal modelling of real estate data address this issue by accounting for the spatial dependence among observations and the fact that only past transactions can influence any given transaction in the dataset. The general spatiotemporal model shown in Eq. 2 conditions all property sales on previous neighbouring transactions and a property’s own hedonic characteristics.

W is the n x n spatial weight matrix that models the spatial dependence structure in the data and ρ is the spatial autoregressive parameter vector of dimensions n × 1. The spatiotemporal modelling approach starts by decomposing W into a spatial matrix S which specifies spatial interactions among all observations⁵ and a temporal matrix T that specifies temporal relations among previous observations only. For the specification of the elements s_ij (i = 1…n; j = 1…n) of the spatial matrix S we use a negative exponential weighting scheme based on the spatial distance d_ij (in kilometres) between any two observations (i, j) (Eq. 3).⁶ The main rationale behind this choice is a theoretical one informed by the nature of the data. Negative exponential functions do not suffer from the point discontinuity problem encountered with the more common inverse distance weighing schemes for the case d_ij = 0. This happens when any two transactions are recorded in the same building (same x and y coordinates) which is a common phenomenon in our dataset considering we have 627 transactions in 33 buildings.

For the specification of the elements τ_ij (i = 1…n; j = 1…n) of the temporal weight matrix we use an inverse distance weighting scheme (Eq. 4) to give more weight to observations happening closer in time. We chose the inverse time distance weighing scheme based on applications of related spatiotemporal studies since the literature does not suggest specific functional forms for both space and time matrixes. In the next section we discuss results with different weight matrix specification both in weighing functions (inverse space distance and negative exponential for time), and in the fine tuning of the above specified functions.where t_ij is the temporal distance (in months) between two observations. The conditioning i > j in both S and T and the temporal ordering of the data ensures dependence only on past observations resulting in lower triangular matrices with zeros on the diagonal. This is consistent with the unidirectional nature of temporal dependence.

In combining the two effects into one spatiotemporal matrix Φ we use the unit by unit (Hadamard) matrix production (Φ = S ʘ T) which also ensures the strict lower triangular nature of Φ that enables all conditioning only on past sales. Based on a priori information about market activity and the economic behaviour of market actors⁷ we specify S for the five nearest spatial neighbours, T with a cut-off point of two quarters and Φ for the ten nearest spatiotemporal neighbours. The final form of W is thus a linear combination of the spatial, temporal and spatiotemporal weights as shown in Eq. 5 where ρ, ν and ψ are the n ×1 vectors of the respective parameters to be estimated.

The final spatiotemporal autoregressive model is given in Eq. 6

Where, the spatial multiplier used in the maximum likelihood estimation is given by: (Ι − ρS−νT − ψΦ) and X is a general matrix of all independent variables. In the literature multiple versions of the maximum likelihood function appear. To ensure continuity of the estimation procedure we adopt the concentrated log likelihood function explained in Pace et al. (2000) which depends on two terms, the log determinant of the spatial multiplier ln|(Ι − W)| and the Squared Sum of Errors (SSE). For model specifications conditional upon previous observations (first period in time series/spatial panels) the so called ‘conditional likelihood’ has a great computational appeal since the value of the determinant of the spatial multiplier is one and its log is zero. This ‘disappearance’ of the spatial multiplier term from the equation shifts the focus from that of maximizing the log likelihood function to that of minimizing the SSE, essentially an OLS approach (Ripley 1981; Upton and Fingleton 1985; Pace et al. 2000; Elhorst 2001; LeSage and Pace 2009). A typical assumption of this approach is the impossibility of instantaneous/contemporaneous interaction among the phenomena under investigation (Upton and Fingleton 1985). In real estate data analysis the temporal resolution is on a day (transaction date basis) however, time is represented as a discrete process, generally on a monthly/quarterly basis. This provides further methodological opportunities to test the existence of such interactions based on this specification of the temporal unit (see Thanos et al. 2016 for an example). The next section proceeds with the empirical analysis of the data based on the framework outlined above.

The vertical structure of cities is an important economic aspect that mainstream urban economics has neglected for the past five decades. This paper contributes to an emerging body of knowledge on the economics of tall commercial office buildings through empirical evidence about rent premiums, vertical sorting across different industry sectors and their willingness to pay for either view amenities or status benefits of height. This is the first paper that analyses vertical location premiums after holding view constant and finds that the non-view, firm-level signalling and other factors constitute roughly 70% of the vertical premium while view and industry-level variations account for 27% and 3% of the vertical location respectively. Other key findings relate to a strong vertical rent gradient within buildings that is even stronger for the top five floors in the office market of Amsterdam. Individual industry sector outcomes point to significant rent premiums paid by ‘Law Firms’, tenants operating in the ‘Consultancy & Management’ and ‘Finance’ sectors compared to the benchmark of ‘Other Sectors’. Evidence of vertical sorting across industries was relatively weak with strong and consistent evidence only related to ‘Law Firms’ located in higher floor levels and this sector having the highest output per FTE among the analysed categories. ‘Consultancy & Management’ firms have consistent outcomes related to rent premiums and sector productivity while evidence on their vertical sorting on higher floor levels was relatively weak. Results regarding ‘Insurance Carriers’ located on top floors and the ‘ICT’ sector having the second highest output per job were not further supported by their paid rent levels which is an outcome that calls for further research.

Additional findings on willingness to pay for either view or status aspects of height shed further light on sector economic strength deriving from productivity and marginal prices of the two respective height perks. ‘Law’ and ‘Consultancy & management’ firms consistently showed the highest willingness to pay for both view and status while also being the first and third most productive sector respectively. This is well in line with the Roback model of wage capitalization into prices. In contrast, ICT firms showed the lowest willingness to pay for benefits deriving from locating on higher floors despite being the second most productive sector. This finding is a notable exception to the hypothesis forwarded by Liu et al. 2018 that high-productivity firms consistently locate on higher floors. Insurance carriers on the other hand, show preference for status over view in their vertical sorting. An interesting feature emerging from the research is the very strong delineation of submarkets in the Amsterdam office market with horizontal sorting/clustering into submarkets being a very important predictor of headline commercial office rents. Future research should focus on investigating the nature of the non-view premium attributable to variations at the firm level and whether the results on wage capitalisation into rents and causes of vertical sorting among industries can be replicated with effective rents. This is an important feature in the commercial office market which can also be used to test the performance of spatiotemporal models in the light of this study’s outcomes.

The findings of this study are of significance to practitioners and policy makers involved in designing urban development policies related, among others, to zoning, land use planning and property-taxation-based financing. Real estate practitioners will find the insights on the causes of vertical sorting, floor level and industry sector-related premiums of high importance during the rental contract negotiation process. In this context, this paper provides additional evidence that improves transparency in the bargaining process among two interested parties. The contribution to the related academic fields is twofold through empirical evidence on the economics of tall buildings, and spatiotemporal modelling of commercial office properties. From the economics of tall buildings perspective particularly the decomposition of the height premium according to prevailing theories and the causes of vertical sorting of industries are notable contributions. Both fields represent exciting areas of research calling for novel, more comprehensive data mining techniques and methodological approaches.