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Research Trend of the Application of Artificial Neural Network in
Property Valuation
Rotimi Boluwatife Abidoye, rotimi.abidoye@connect.polyu.hk
Department of Building and Real Estate, The Hong Kong Polytechnic, Hong Kong
Albert P. C. Chan, albert.chan@polyu.edu.hk
Department of Building and Real Estate, The Hong Kong Polytechnic, Hong Kong
Abstract
The Artificial Neural Network (ANN) technique has been applied and found useful for solving
forecasting problems in different property markets around the world. However, the trend of ANN’s
application in property valuation research has been undocumented. Therefore, this study aims to
systematically review the extant literature on the application of ANN to property valuation. The analysis
of the retrieved articles revealed that a seminal study in this area was reported in 1991. To date, the
technique has continued to gain popularity amongst real estate researchers. Most of the reviewed articles
originated from developed countries, particularly the US and the UK, although a few studies emanated
from emerging economies. Most of the authors that contributed to the publications are affiliated to
university faculties and most of the studies found ANN to have outperformed other appraisal techniques,
in terms of accuracy. The gaps identified in this study need to be addressed in order to achieve sustained
growth in property appraisal practice on a global scale.
Keywords: Artificial Neural Network, real estate, property market, property valuation, review
1 Introduction
In arriving at the value of a property, valuers/appraisers normally adopt one or more valuation
techniques. These approaches, especially the traditional ones, have been proven to be inadequate in
producing objective valuation figures (Zurada et al 2006). For instance, the hedonic pricing model
(HPM) that has been widely adopted in real estate valuation research (Bender et al 2000), and even in
practice (McCluskey et al 1997), cannot capture the underlying nonlinear relationship that exists
between property value and property attributes (Do & Grudnitski 1992). Hence, the estimation of
inaccurate and unreliable valuation figures. Reliable and accurate prediction of property value is of
valuable interest to real estate stakeholders for one important reason: investment decisions are based on
estimated valuation figures. In pursuit of a reliable and accurate valuation estimation, researchers have
adopted artificial intelligence (AI) into property valuation in order to improve the accuracy of valuation
estimates, and one of such AI technique is the artificial neural network (ANN) technique.
ANN is an AI technique programmed to function like the human neural network. ANN has a
learning ability just like the human brain neurons. It has been adopted in different fields of studies for
prediction, pattern recognition, forecasting, classification and nonlinear mapping, among others (Cechin
et al 2000; Paliwal & Kumar 2009), and has produced outstanding reliable results (Paliwal & Kumar
2009). The technique was introduced to the real estate research domain in the early 1990s and till now
scholars in different real estate markets around the world continue to adopt the ANN technique in
property price forecasting. Scholars (Pagourtzi et al 2003; Limsombunchai et al 2004; Guan et al 2008,
amongst others) have reported its outstanding performance in property valuation and even over other
AI techniques, such as the fuzzy logic system (FLS), Autoregressive Integrated Moving Average
(ARIMA) and spatial analysis. Despite its wide embrace by real estate scholars, the extent of the
application of ANN in property valuation research remains unknown. Therefore, this study is aimed at
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
systematically reviewing published studies focused on the application of ANN to property valuation.
The findings of the proposed research will highlight the current situation and identify the gaps in this
research area. This present study will reveal the active researchers in this field, their affiliations, the
countries of origins of these articles, the annual trend of the publication output and a summary of the
research findings of these studies.
2 Artificial neural network: A brief history
The history of the ANN technique can be categorized into four stages namely beginning of neural
networks – the 1940s; the first golden age – the 1950s to 1960s; the quiet years – the 1970s; and the
renewed enthusiasm period of the 1980s – now (Yadav et al 2015). The final stage is the present day
neural network research area where the application of the technique exploded and received more
attention by scholars. The study of McCulloch & Pitts (1943) was the first to employ the ANN technique
to demonstrate the ‘threshold logic’ in the field of mathematics. Thereafter, the technique has been
adopted successfully in different fields of studies (Zhang et al 1998). These areas of research include
but are not limited to, health (Wesolowski & Suchacz 2012), engineering (Yuan & Guangchen 2011),
marketing (Chiang et al 2006), the stock market (Eriki & Udegbunam 2013), and tourism demand
(Burger et al 2001), amongst others.
In the real estate domain, the seminal study of Borst (1991) was the first to apply the ANN technique
to property appraisal. The author found that ANN is reliable and accurate for property value estimation,
but recommended that more research efforts be invested in the application of ANN. Since it has been
established that the ANN technique handles the shortcomings of most of other appraisal techniques (Do
& Grudnitski 1992), researchers in different real estate markets around the world have investigated its
application in their domains and have mostly reported a positive result (Limsombunchai et al 2004).
The trend of these articles is reported in the present study.
3 Research method
In order to achieve the objectives of this study, a systematic literature review was adopted. This is to
ensure that both the current state of knowledge and also the research gaps are identified from the existing
literature (Mayer 2009), and also to identify the major scientific contributions to the subject under
investigation (Tranfield et al 2003). By adopting this approach, references will be made to previous
studies in justifying assertions, making comparison and drawing inferences (Denscombe 2014).
Therefore, articles that have utilized the ANN technique solely or in comparison with other appraisal
techniques in property valuation research were retrieved from online databases and search engines such
as ScienceDirect, Taylor & Francis, Springer and Google Scholar. Studies that adopted its hybrid
application with other AI techniques, for instance, Liu et al (2006); Guan et al (2008), amongst others,
were excluded from the search. This approach is similar to that adopted by Yi & Chan (2013), in
reviewing labor productivity research in construction journals.
The search started with the input of the following search words into the databases and search
engines artificial neural network, property price prediction, artificial neural network in property
appraisal, real estate price forecasting, artificial intelligence property appraisal, multilayer perception,
real estate price modeling, multilayer perception in property price forecasting, modeling property price
and mass real estate appraisal. The obtained articles were scrutinized in order to ensure that only articles
that met the inclusion criteria were eventually retrieved. However, only studies published in the form
of journal articles, conference proceedings and book sections were found to be relevant for the present
study, hence, these are the types of articles reviewed. It is worth mentioning that in a situation where
the same research finding was published in a journal as well as a conference proceeding, the one
published in the journal was chosen. For instance, the study of Limsombunchai (2004) and
Limsombunchai et al (2004), are the conference proceeding and journal article, respectively.
At the end of the search exercise, 52 articles were eventually retrieved. These articles were
subjected to analysis and the findings were presented using descriptive statistics to establish the active
authors of the articles, the affiliations of the authors, the number of research papers published annually,
the origins of these publications (i.e. the study area), the collaborations that exists amongst researchers
and the publication outlets of the articles.
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
4 Results and discussion
4.1 Active authors
Considering the turnout of publications by numerous scholars, a total of 135 authors contributed to the
publication of the reviewed articles. It is evident that most of those authors have published at most one
article during the study period, although some have published more than one article. The details of
authors that have published at least two articles are presented in Table 1. McCluskey, W. and Jenkins,
D. H. are the highest contributors to this research topic, with four articles each to their credit. In the
same vein, Borst, R. A. and Ware, J. A. have published three articles each. These scholars can safely be
referred to as active contributors to this research area.
Table 1 Active contributors of publications
Authors
Studies
No. of
publications
McCluskey, W.
McCluskey (1996), McCluskey & Borst (1997)
McCluskey et al (2012); McCluskey et al (2013)
4
Jenkins, D. H.
Lewis et al (1997); Jenkins et al (1999); Panayiotou et
al (2000); Wilson et al (2002)
4
Borst, R. A.
Borst (1991); Borst (1995); McCluskey & Borst (1997)
3
Ware, J. A.
Lewis et al (1997); Jenkins et al (1999); Wilson et al (2002)
3
Worzala, E. M.
Worzala et al (1995); Lenk et al (1997)
2
Lenk, M. M.
Worzala et al (1995); Lenk et al (1997)
2
Lewis, O. M.
Lewis et al (1997); Jenkins et al (1999)
2
Rossini, P.
Rossini (1997); Rossini (1998)
2
Levitan, A. S.
Zurada et al (2006); Zurada et al (2011)
2
Zurada, J. M.
Zurada et al (2006); Zurada et al (2011)
2
Guan, J.
Zurada et al (2006); Zurada et al (2011)
2
McCord, M.
McCluskey et al (2012); McCluskey et al (2013)
2
Kauko, T.
Kauko et al (2002); Kauko (2003)
2
Haran, M.
McCluskey et al (2012); McCluskey et al (2013)
2
Davis, P. T.
McCluskey et al (2012); McCluskey et al (2013)
2
McIhattan, D.
McCluskey et al (2012); McCluskey et al (2013)
2
From the analysis of the articles, it can be established that researchers have been collaborating in this
study area. This is evident from 41 (79%) out of the 52 reviewed articles having been co-authored by
at least two people, with the maximum number of authors being five (see Figure 1), whereas only 11
(21%) were sole-authored. The mean number of authors is 2.9, which indicates that most of the papers
were authored by three scholars. It can be suggested that for outstanding results to be achieved in this
research endeavor, collaboration should not be overlooked by researchers.
11
14
17
5 5
12345
Number of authors
Figure 1 Authors’ collaborations
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
4.2 Affiliations of the authors
The analysis of the articles shows that the authors were either affiliated to a university, polytechnic,
research institute, government organization or practicing firm. However, the statistics presented in
Figure 2 reveals that 87% (118) of the authors were affiliated to a university, 5% were affiliated to a
polytechnic, 4% researchers were domiciled in research institutes, while 2% were affiliated to
government organizations and practicing firms, respectively. This corroborates previous related studies
(Adewunmi & Olaleye 2011; Holt et al 2015; Utama et al 2016) that found that researchers domiciled
in universities are the highest contributors to real estate and built environment research. Research is
essential for providing a body of knowledge for a particular discipline (Hemsley-Brown & Sharp 2003),
so it is imperative that industry based real estate professionals should be actively involved in research
in order to achieve a sustainable real estate practice.
4.3 Origin of the publications
The reviewed articles were conducted in different real estate markets of the world. Figure 3 shows that
all the articles emanated from 22 countries in the world. Of these 22 nations, one article each was
published in almost half (10) of the countries. The highest number of publications originated from the
United States, where about 20% of the total number of publications reviewed were conducted. This is
also the case in the United Kingdom, from where 17% of the articles reviewed emanated. It can be
concluded that most of the articles published in this research area emanated from developed countries.
This can be substantiated with the next highest number of publications of three articles each originating
from Australia, Hong Kong and Spain, respectively. Some studies were conducted in a few developing
countries, with mostly one article to their credit. The low adoption of the ANN technique in these
emerging property markets may be attributed to a lack of know-how in its application for property
valuation in these developing countries (Abidoye & Chan 2016). And as noted in the present study that
collaboration between scholars in this research area has brought about many results, researchers
domiciled in developing countries could explore the possibility of collaborating with established
scholars in developed countries. This could result in a global exposition of researchers in developing
countries to AI appraisal techniques.
Universities
87%
Polytechnics
5%
Government organizations
2% Research institutes
4% Practicing firms
2%
Figure 2 Authors’ affiliations
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
4.4 Annual trend of research output
The first application of ANN to property valuation was reported in 1991 (see Borst 1991).
Subsequently, various scholars from different parts of the world have applied ANN to property
valuation research. This is also the case in the finance research area where the ANN technique was first
adopted at the beginning of the early 1990s (Ramos & Martínez 2013). Figure 4 shows the annual trend
of research output from 1991 to 2015. From Figure 4, it is clear that there has been fluctuations in the
number of articles published between 1991 and 2015. In most years, mostly one and two publications
were recorded with the exception of 1997 and 2011 where four and six articles were published,
respectively. On a decade basis, 16 articles were published in the first decade (1991-2000) of the
introduction of ANN to real estate appraisal. This figure rose to 20 between 2001-2010, probably due
to its rapid popularity amongst real estate researchers (Adhikari & Agrawal 2013). In the present decade
(2011- 2020) of the life cycle of the technique in the real estate domain, 16 articles have been published
so far in just five years into the decade. Taken together, since 2011 when the highest number of
publications was recorded, there has been a drastic drop in the annual publication output which has been
fluctuating between two and three annually. It is expected that the number of publications should
increase yearly since not all the real estate markets of the world have been modeled using the ANN
technique. Continuous effort is needed to be invested in automating property valuation globally, and
the exploration of the adoption of AI techniques such as the ANN technique should therefore, be
sustained.
10
1
9
3
1 1
3
2
3
1
2222
1
2
1
2
1111
0
2
4
6
8
10
12
Numbers of publications
Figure 3 Origin of publications
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
4.5 Publication outlets
From the analysis of the reviewed articles, 43 articles were published in different journal outlets, eight
were proceedings from international conferences, while one is a book section. Most of the journal
outlets have published just one article over the study period, whereas some have published more than
one article. Table 2 shows the journals that have published more than one article since 1991 to 2015.
Four articles each have been published in the Journal of Real Estate Research and Journal of Property
Research, while three were published in the Journal of Property Valuation and Investment. This
indicates that research outputs are likely to be published in these outlets.
Table 2 Popular journal outlets
Name of journal
No. of publications
Journal of Real Estate Research
4
Journal of Property Research
4
Journal of Property Valuation and Investment
3
4.6 Summary of research findings
The findings of each article was analyzed in terms of the performance of the ANN technique in property
valuation. This was examined in order to establish 1) if the performance of the ANN technique is better
than other approaches, 2) if the performance of ANN is at par with other techniques, and 3) if other
appraisal techniques perform better than the ANN technique. In about 82% of the articles reviewed,
the ANN technique was reported to perform better than other techniques. In 11% of the cases, the
performance of ANN was equal to that of other valuation approaches, while 7% of the studies reported
that the ANN technique did not outperform other valuation techniques. This corroborates the finding of
previous similar studies (Paliwal & Kumar 2009) where in most of the articles reviewed, the outstanding
performance of the ANN technique over other methods was confirmed. Despite the superior predictive
performance of ANN models reported in property valuation research, the technique is classified as a
‘black-box’ model (McGreal et al 1998; Lam et al 2008), implying that it is difficult to explain what
goes on in the hidden layer of the model. However, the recent development of techniques such as
sensitivity analysis (Cortez 2010) have proven that the internal workings of AI models can be
understood. A sensitivity analysis of AI models (such as ANN) has been proven to be particularly useful
in past studies found in the literature (Cortez et al 2009; Tinoco et al 2011).
0
1
2
3
4
5
6
7
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Annual publications
Figure 4 Annual output of publications
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
5 Conclusion
A review of the studies that adopted the ANN technique for property valuation was documented in this
study. Articles that were retrieved from online databases and search engines were analyzed in order to
present the history and trend in the research area. The technique was first introduced into the real estate
appraisal domain in 1991 and since then, it has been gaining greater acceptance by real estate scholars.
It was found that majority of the publications reviewed were conducted by real estate scholars domiciled
in university faculties, while, real estate professionals practicing in other organizations have contributed
little to the research debate. Similarly, most of the articles were conducted in developed countries, with
few emanating from developing countries. The low usage of this technique in developing economies
could be due to the lack of the knowledge in the application of the ANN technique, unavailability of a
rich database of property sale transactions and/or concerns regarding the reliability of available data.
There is a need for real estate researchers and professionals in emerging property markets to collaborate
with their counterparts in the developed world to bridge this gap in knowledge. Over the study period
(1991-2015), annual research output fluctuated. However, the highest annual publication was recorded
in 2011, with the annual output declining since then. It will be interesting to investigate the reason(s)
for this decline, probably there has been a paradigm shift to other contemporary appraisal technique(s).
In most of the studies reviewed, the outstanding predictive and forecasting ability of the ANN technique
was established, which corroborates the existing literature. However, it must be noted that this does not
suggest that application of ANN will address all real-world valuation problems. This is because there
is a need for expert experience during the process of model development (for example the selection of
input variables). Also, valuation techniques are embedded with various degrees of strengths and
weaknesses.
Due to the successes recorded in this research area in different parts of the world during the study period,
there is a need for conscious effort to propagate its exploration in other real estate markets that have not
been optimally explored. When this is achieved, in theory, it could be easier to introduce it to the real
estate appraisal practice and that could lead to a sustainable global real estate practice. This study cannot
be said to be exhaustive based on the number of articles reviewed, as articles not indexed in the
databases searched might have been omitted. Also, the choice of the search words might not have
captured some relevant studies that do not have these words in their titles, keywords or abstracts.
However, the retrieved articles were subjected to a systematic analysis, so as to draw useful inferences
from the pool of publications. In filling the gap of the low application of ANN in developing countries,
effort will be made in future research in modeling the Nigerian real estate market, its being an emerging
real estate market and the biggest economy in Africa.
Acknowledgements
The authors sincerely acknowledge the Research Grants Council of Hong Kong (SAR) and the
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, for
providing financial and material support towards this research.
References
Abidoye, R. B. and Chan, A. P. C. (2016) A survey of property valuation approaches in Nigeria. Property
Management, 34(5), pp. 364-382.
Adewunmi, Y. A. and Olaleye, A. (2011) Real estate research directions and priorities for Nigerian institutions.
Journal of Real Estate Practice and Education, 14(2), pp. 125-140.
Adhikari, R. and Agrawal, R. (2013) An introductory study on time series modeling and forecasting.
Available: http://arxiv.org/ftp/arxiv/papers/1302/1302.6613.pdf [Accessed 12 July 2016].
Bender, A., Din, A., Hoesli, M. and Brocher, S. (2000) Environmental preferences of homeowners: Further
evidence using the AHP method. Journal of Property Investment & Finance, 18(4), pp. 445-455.
Borst, R. A. (1991) Artificial neural networks: The next modelling/calibration technology for the assessment
community. Property Tax Journal, 10(1), pp. 69-94.
Borst, R. A. (1995) Artificial neural networks in mass appraisal. Journal of Property Tax Assessment &
Administration, 1(2), pp. 5-15.
Burger, C., Dohnal, M., Kathrada, M. and Law, R. (2001) A practitioners guide to time-series methods for tourism
demand forecasting: A case study of Durban, South Africa. Tourism Management, 22(4), pp. 403-409.
Cechin, A., Souto, A. and Gonzalez, A. M. (2000). Real estate value at Porto Alegre city using artificial neural
networks. Proceedings of the 6th Brazilian Symposium on Neural Networks. IEEE, Rio de Janeiro,
Brazil, 22-25 November. pp. 237-242.
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
Chiang, W. K., Zhang, D. and Zhou, L. (2006) Predicting and explaining patronage behavior toward web and
traditional stores using neural networks: A comparative analysis with logistic regression. Decision
Support Systems, 41(2), pp. 514-531.
Cortez, P. (2010). Data mining with neural networks and support vector machines using the R/rminer tool.
Proceedings of the Industrial Conference on Data Mining. Springer, Berlin, Germany. pp. 572-583.
Cortez, P., Cerdeira, A., Almeida, F., Matos, T. and Reis, J. (2009) Modeling wine preferences by data mining
from physicochemical properties. Decision Support Systems, 47(4), pp. 547-553.
Denscombe, M. (2014). The good research guide: For small-scale social research projects (5th ed.). England:
Open University Press.
Do, A. Q. and Grudnitski, G. (1992) A neural network approach to residential property appraisal. The Real Estate
Appraiser, 58(3), pp. 38-45.
Eriki, P. O. and Udegbunam, R. (2013) Predicting corporate distress in the Nigerian stock market: Neural network
versus multiple discriminant analysis. African Journal of Business Management, 7(38), pp. 3856-3863.
Guan, J., Zurada, J. and Levitan, A. S. (2008) An adaptive neuro-fuzzy inference system based approach to real
estate property assessment. Journal of Real Estate Research, 30(4), pp. 395-421.
Hemsley-Brown, J. and Sharp, C. (2003) The use of research to improve professional practice: A systematic
review of the literature. Oxford Review of Education, 29(4), pp. 449-471.
Holt, G. D., Akintoye, A. and Davis, P. (2015) Editorial: Analysis of papers published in the Journal of Financial
Management of Property and Construction 2010-2015. Journal of Financial Management of Property
and Construction, 20(2), pp. 106-115.
Jenkins, D., Lewis, O., Almond, N., Gronow, S. and Ware, J. (1999) Towards an intelligent residential appraisal
model. Journal of Property Research, 16(1), pp. 67-90.
Kauko, T. (2003) On current neural network applications involving spatial modelling of property prices. Journal
of Housing and the Built Environment, 18(2), pp. 159-181.
Kauko, T., Hooimeijer, P. and Hakfoort, J. (2002) Capturing housing market segmentation: An alternative
approach based on neural network modelling. Housing Studies, 17(6), pp. 875-894.
Lam, K. C., Yu, C. Y. and Lam, K. Y. (2008) An artificial neural network and entropy model for residential
property price forecasting in Hong Kong. Journal of Property Research, 25(4), pp. 321-342.
Lenk, M. M., Worzala, E. M. and Silva, A. (1997) High-tech valuation: Should artificial neural networks bypass
the human valuer? Journal of Property Valuation and Investment, 15(1), pp. 8-26.
Lewis, O. M., Ware, J. A. and Jenkins, D. (1997) A novel neural network technique for the valuation of residential
property. Neural Computing & Applications, 5(4), pp. 224-229.
Limsombunchai, V. (2004). House price prediction: Hedonic price model vs. artificial neural network.
Proceedings of the New Zealand Agricultural Resource Economics Society 2004 Conference. NZARES,
Blenheim, New Zealand, 25-26 June. pp. 1-15.
Limsombunchai, V., Gan, C. and Lee, M. (2004) House price prediction: Hedonic price model vs. artificial neural
network. American Journal of Applied Sciences, 1(3), pp. 193-201.
Liu, J.-G., Zhang, X.-L. and Wu, W.-P. (2006) Application of fuzzy neural network for real estate prediction. In
J. Wang, Z. Yi, J. M. Zurada, B.-L. Lu and H. Yin Ed. Advances in Neural Networks-ISNN 2006.
Springer, Berlin Heidelberg, pp. 1187-1191.
Mayer, P. (2009) Guidelines for writing a review article. Available: http://ueberfachliche-
kompetenzen.ethz.ch/dopraedi/pdfs/Mayer/guidelines_review_article.pdf [Accessed 12 July 2016].
McCluskey, W. (1996) Predictive accuracy of machine learning models for the mass appraisal of residential
property. New Zealand Valuers Journal, 16(1), pp. 41-47.
McCluskey, W., Davis, P., Haran, M., McCord, M. and McIlhatton, D. (2012) The potential of artificial neural
networks in mass appraisal: The case revisited. Journal of Financial Management of Property and
Construction, 17(3), pp. 274-292.
McCluskey, W., Deddis, W., Mannis, A., McBurney, D. and Borst, R. (1997) Interactive application of computer
assisted mass appraisal and geographic information systems. Journal of Property Valuation and
Investment, 15(5), pp. 448-465.
McCluskey, W. J. and Borst, R. A. (1997) An evaluation of MRA, comparable sales analysis, and ANNs for the
mass appraisal of residential properties in Northern Ireland. Assessment Journal, 4(1), pp. 47-55.
McCluskey, W. J., McCord, M., Davis, P., Haran, M. and McIlhatton, D. (2013) Prediction accuracy in mass
appraisal: A comparison of modern approaches. Journal of Property Research, 30(4), pp. 239-265.
McCulloch, W. S. and Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity. The bulletin
of mathematical biophysics, 5(4), pp. 115-133.
McGreal, S., Adair, A., McBurney, D. and Patterson, D. (1998) Neural networks: The prediction of residential
values. Journal of Property Valuation and Investment, 16(1), pp. 57-70.
Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T. and French, N. (2003) Real estate appraisal: A review of
valuation methods. Journal of Property Investment & Finance, 21(4), pp. 383-401.
Abidoye & Chan (2016) Review of ANN Application in Property Valuation
Proc. of the 33rd CIB W78 Conference 2016, Oct. 31st – Nov. 2nd 2016, Brisbane, Australia
Paliwal, M. and Kumar, U. A. (2009) Neural networks and statistical techniques: A review of applications. Expert
Systems with Applications, 36(1), pp. 2-17.
Panayiotou, P., Pattichis, C., Jenkins, D. and Plimmer, F. (2000). A modular artificial neural network valuation
system. Proceedings of the 10th Mediterranean Electrotechnical Conference. IEEE, Lemesos, Cyprus,
29-31 May. pp. 457-460.
Ramos, E. G. and Martínez, F. V. (2013) A review of artificial neural networks: How well do they perform in
forecasting time series? Journal of Statistical Analysis, 6(2), pp. 7-18.
Rossini, P. (1997) Artificial neural networks versus multiple regression in the valuation of residential property.
Australian Land Economics Review, 3(1), pp. 1-12.
Rossini, P. (1998). Improving the results of artificial neural network models for residential valuation. Proceedings
of the 4th Annual Pacific-Rim Real Estate Society ConferencePerth, Western Australia, 19-21 January
pp. 1-18.
Tinoco, J., Correia, A. G. and Cortez, P. (2011) Application of data mining techniques in the estimation of the
uniaxial compressive strength of jet grouting columns over time. Construction and Building Materials,
25(3), pp. 1257-1262.
Tranfield, D., Denyer, D. and Smart, P. (2003) Towards a methodology for developing evidence‐informed
management knowledge by means of systematic review. British Journal of Management, 14(3), pp. 207-
222.
Utama, W. P., Chan, A. P., Zahoor, H. and Gao, R. (2016) Review of research trend in international construction
projects: A bibliometric analysis. Construction Economics and Building, 16(2), pp. 71-82.
Wesolowski, M. and Suchacz, B. (2012) Artificial neural networks: Theoretical background and pharmaceutical
applications: A review. Journal of AOAC International, 95(3), pp. 652-668.
Wilson, I. D., Paris, S. D., Ware, J. A. and Jenkins, D. H. (2002) Residential property price time series forecasting
with neural networks. Knowledge-Based Systems, 15(5), pp. 335-341.
Worzala, E., Lenk, M. and Silva, A. (1995) An exploration of neural networks and its application to real estate
valuation. Journal of Real Estate Research, 10(2), pp. 185-201.
Yadav, N., Yadav, A. and Kumar, M. (2015) History of neural networks. In K. Janusz and P. Warsaw Ed. An
introduction to neural network methods for differential equations. Springer, London, pp. 114.
Yi, W. and Chan, A. P. (2013) Critical review of labor productivity research in construction journals. Journal of
Management in Engineering, 30(2), pp. 214-225.
Yuan, R. and Guangchen, B. (2011) New neural network response surface methods for reliability analysis.
Chinese Journal of Aeronautics, 24(1), pp. 25-31.
Zhang, G., Patuwo, B. E. and Hu, M. Y. (1998) Forecasting with artificial neural networks: The state of the art.
International journal of forecasting, 14(1), pp. 35-62.
Zurada, J., Levitan, A. and Guan, J. (2011) A comparison of regression and artificial intelligence methods in a
mass appraisal context. Journal of Real Estate Research, 33(3), pp. 349-387.
Zurada, J. M., Levitan, A. S. and Guan, J. (2006) Non-conventional approaches to property value assessment.
Journal of Applied Business Research, 22(3), pp. 1-14.