Impacts of digitization on real estate sector jobs
| Published date | 19 March 2020 |
| Pages | 47-83 |
| Date | 19 March 2020 |
| DOI | https://doi.org/10.1108/JPIF-09-2019-0125 |
| Subject Matter | Property management & built environment,Real estate & property,Property valuation & finance |
| Author | Daniel Piazolo,Utku Cem Dogan |
Impacts of digitization on real
estate sector jobs
Daniel Piazolo and Utku Cem Dogan
Technische Hochschule Mittelhessen, Friedberg, Germany
Abstract
Purpose –Previous research on automation and job disruption is only marginally related to the real estate
industry and its characteristics. This study investigates the effects of digitization on jobs in German real estate
sector, in order to assess the proportion of jobs threatened to be replaced by automation. Since Germany is the
largest EU economy insights for the German real estate market allow a first approximation for Europe.
Design/methodology/approach –An extensive database of the German Federal Employment Agency
containing job definitions and occupation titles is matched with real estate criteria to create a subset with the
relevant real estate occupations. This data is combined with a database of the German Institute of Employment
Research reflecting to what extent tasks within jobs can be automated by current technical capabilities.
Findings –For the 286 identified occupations within the real estate sector a weighted average of 47 percent
substitution probability through current technological capabilities is derived for tasks within the examined
occupations.
Practical implications –This contribution indicates the extent of the structural change the real estatesector
has to face due to digitization: One out of two real estate jobs will have to be re-created.
Originality/value –This research quantifies the magnitude of the job killer aspect of digitization in the real
estate sector.
Keywords Employment, Digitization, Automation, Structural change, Disruption, Substitution potential
Paper type Research paper
1. The challenge for jobs
When the popularmedia routinely run article titles like “A WorldWithout Work”(Thompson,
2015), there is a strongindication that an issue has reached a significantlevel of critical mass.
There is no disputing that new technologies (NT) are disrupters to labor and vocational
categories, however, the levels of disruption and its impact are not agreed on. At the same
time, the utilization of NT’s leads to new possibilities and job areas that are being created.
These jobs differ in their complexity and demands, and therefore are often better paid.
Innovation and its impact on labor is part of structural change. Pessimistic views in the
last year are frequently based on insights from Frey and Osborne (2013) who quantified the
impacts of NT’s on labor markets in the United States. Accordingly, 47 percent of jobs are
subject of being substituted by NT’s until 2030. Various studies have used the results of Frey
and Osborne by transferring the codes of American occupations to other countries (Bonin
et al., 2015;Dengler and Matthes, 2015;Brzeski and Burk, 2015), according to the International
Standard Classification of Occupations (ISCO). However, these studies follow the approach
that it is not entire professions that can be replaced by NT’s, but rather activities leading to a
significantly lower share of jobs that are being threatened to be substituted by computers.
Arntz et al. (2017) maintain that there is evidence to support an impact of a 9 percent to 11
percent job loss in OECD countries caused by digitization. For the purpose of serving as a
Digitization
and German
real estate jobs
47
© Daniel Piazolo and Utku Cem Dogan. Published by Emerald Publishing Limited. This article is
published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce,
distribute, translate and create derivative works of this article (for both commercial and non-commercial
purposes), subject to full attribution to the original publication and authors. The full terms of this licence
may be seen at http://creativecommons.org/licences/by/4.0/legalcode
This paper forms part of a special section “PropTech and Entrepreneurship - Innovation in Real
Estate II”, guest edited by Dr Larry Wofford, Dr David Wyman, Dr Elaine Worzala.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1463-578X.htm
Received 27 September 2019
Revised 18 December 2019
Accepted 19 December 2019
Journal of Property Investment &
Finance
Vol. 39 No. 2, 2021
pp. 47-83
Emerald Publishing Limited
1463-578X
DOI 10.1108/JPIF-09-2019-0125
literature review to a wider study that specifically aims at the impact of NT’s on real estate,
the focus of the following will ask whether there is anything emergent about the current
transformations and a long history of previous disruptive phases.
Wage stagnationhas been an important economic force that has occurred steadilysince the
1980’s among Organization for Economic Co-operation and Development (OECD) nations.
Stagnation means that wagesare relatively flat and do not rise at the rate of inflation. Among
these OECD countries, this is a reversal of economic trends that have defined most of the
Twentieth Century and was acceleratedin particular, during the post-World War Two period.
It is a period where the most prominentfactor of this trend is that each subsequent generation
had more wealth and disposableincome than the previous. The consequence of this stagnation
is that little growth is occurringand lower relative wages means that capital is not circulating
in the economy giventhat people simply have less disposable income to spend (Picketty, 2014).
The economy has stagnated, but capital or profit has increasedand this is explained through
fewer workers creating more profit and also, a shift toward the financialization of the
economy. At the same time, Gregory et al. (2016) conclude that from 2000 to 2010 there has
been a steady increase in labor demand. In spite of this shift on the other hand, rising
unemployment has not been an outcome through the processes of automation that have
occurred so far (Arntz et al., 2017). Within the context of Artificial Intelligence (AI), Machine
Learning (ML) and robotics, these general factors raise a number of important variables for
consideration because they make the impact of these new technologies difficultto measure.
When the data concerning wage stagnation is combined with the analysis of large sets of
tax returns in the US and France over a fifty-year period (Picketty, 2014), a number of
competing theories emerged as plausible explanation for this. One theory was that through
the erosion of the bargaining power of labor wages have stagnated. A second major theory
was that outsourcing of tasks and globalization of production are the most significant drivers
of stagnation. Manufacturing and then service sector jobs have continually been moved from
developed economies to emerging economies because of lower standards for regulation
(Addo, 2016). After a period of stagnation being explained by theories about bargaining
power shifts and globalization, a new set of evidence began to present NT’s as the most
important driver to this trend. However, the wider use of NT has various, mutually conflicting
consequences. Variables of influence like globalization and political policies, cannot be
completely separated from NT’s as a factor for job loss and the de-skilling of labor which has
resulted in lower wage forms of employment. Frey and Osborne (2017) assert that this creates
a greater “polarization”between skilled and unskilled labor and therefore “a hollowing-out of
middle-income jobs”.
The following will examine the level of disruption on the real estate sector in particular.
There is an important and conflicting set of assumptions and conclusions that make this
difficult to determine. For reasons that will be outlined in the Literature Review, this study
will take the task-based approach to automation or substitution potential defined by Dengler
and Matthes, 2015,2016 in order to determine the share of jobs in the German real estate
industry that are affected by digitization. The article concludes with a discussion on the
remaining tasks for human beings in the real estate industry.
2. Literature review
NT’s can be understood as having a long history within the capitalist economy. The
mechanization of labor can be traced to the steam powered machines that replaced hand
weaving in the clothing industry in the late 1700’s and early 1800’s. This economic
transformation to industrialization also contributed to an early reactionary backlash, which
led to protests by workers in England between 1811 and 1816 when these new machines were
blamed for unemployment and low wages. Consequently, the manufacturing equipment was
damaged by workers (DeCanio, 2016;Frey and Osborne, 2017). Industrialization and
JPIF
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automation were furthered when the Ford model of production was invented and then,
quickly adopted by other types of industries. Henry Ford introduced the assembly line model
of production whereby individuals become specialized in only one area of manufacturing and
this division of labor made production more streamlined and created greater output. The
automation of tasks and the deskilling of labor are not new. Like-wise, neither is the criticism
of automation that has historically been based on the loss of employment and the de-skilling
of existing work tasks. However, the counterweight to this trend has been the economic gains
that have emerged as a result. Various areas of employment have been created by the
technological change that has led to an expansion of entire sectors such as electronics and
computer related fields. For example, Bessen (2016) presented a comprehensive data set of
317 types of jobs that were being replaced by automated technologies driven by
computerization and demonstrated that newly created types of employment far exceeded
the losses caused by automation. Based on the structure of industrialization within a
historical context, DeCanio (2016) presents a data analysis of tasks and substitution potential,
and concludes that wage levels will decrease, and that although NT’s create new
opportunities in fields like engineering, the overall outcome will be the de-skilling of labor.
There are a number of criticisms to this historically driven approach to job market
changes. An important criticism of is related to market capitalization and value creation.
West (2018) looks at the relationship between market capitalization and employment and
compares data taken from 1962 to 2017. In 1962 the two largest companies in terms of market
value were AT&T with a value of USD$ 20B (2017) with 564,000 employees and General
Motors with a USD$ 12B (2017) value and 605,000. In 2017, Apple had a market capital share
of USD$ 800B with 116,000 employees and Google/Alphabet had USD$ 670B and 73,992
workers. In other terms, Apple generated forty times the wealth as AT&T with a fifth of the
full-time employees (West, 2018). As a good example of this wealth generation process
achieved by few, two individuals developed Android with less than $ 10,000 and then sold
this in less than a year to Google for a $ 1B and at the point of sale they employed 50
individuals (Madridakis, 2017). While some maintain that more jobs have been created by the
overall computerization (Bessen, 2016), there are important features in current technologies
that have to be considered for future projections. Although in the past there was a link
between employment growth and innovation, in the future other factors specific to new
technologies might generate value without employment growth.
AI can be seen as an example of different value creation in the context of social media and
the platform economy. In 2017, Facebook had a market value of USD$ 441B with 18,770
employees (West, 2018). The Facebook revenue was generated through the use of clients’data
for the purposes of generating advertising, marketing and market research, and the means
for this was the AI employed in data mining/collection and data-analytics. Plat-form models
are achieving the same by having algorithms and not humans connect customers with service
providers and then, collecting a fee through this human-less transaction. Facebook’s market
value to employee ratio is significantly greater than either Apple or Google.
Within the real estate industry, the platform economy and the use algorithms is likewise
growing. Conway (2018) identifies nine major industry areas where 71 software applications
and web-based platforms are emerging that replace human tasks and occupations. A number
of these real estate areas include data analytics and platform applications that connect buyers
and sellers, borrowers and lenders, customers and legal documents, customers and
valuations. These areas are data driven AI applications, thus algorithms rather than people
generate value.
Other areas are using more NT’s and change the tasks performed by the human
employees. The fastest growing area is buildings and operations management where remote
security systems, smart home technologies and robotics used in cleaning and maintenance
are already having a significant impact. Further, new possibilities emerge through 3D
Digitization
and German
real estate jobs
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