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Housing obsolescence in practice; towards a management tool
Nico NieboerOTB Research Institute, Delft University of Technology, NL N.E.T.Nieboer@tudelft.nl
André Thomsen
OTB Research Institute, Delft University of Technology, NL A.F.Thomsen@tudelft.nl
Kees van der Flier
Faculty of Architecture, Dept. Real Estate & Housing, Delft University of Technology, NL C.L.vanderFlier@tudelft.nl
Abstract
Obsolescence, broadly defined as a process of declining performance resulting in the end of the service life, is a serious threat for built property. This is even more the case in present times of reduced investment budgets and relatively small numbers of new-built homes, which increase the relative importance of the existing housing stock. Knowledge about the prevention, the diagnosis and the treatment of obsolescence is therefore of growing importance.
In previous research publications we combined the available knowledge about obsolescence in a conceptual model for further research and for appliance in asset management decision-making, tested the model in a series of varied case studies and combined the findings with an inventory of tools to detect and measure different kinds of obsolescence.
As the next step in our research, we tested the application of the model in the practice of Dutch housing associations in different housing market conditions. Based on interviews with the management and combining the available management data in the model we try to answer the question to what extent the model can be useful to detect, identify and measure obsolescence.
This paper describes the case studies, the way the model was applied, the findings and the outcomes and concludes with answering the research question and the perspective for further research.
Keywords:
2 1. Introduction
Obsolescence can be defined as the process of declining performance of buildings. Given the large investments incorporated in buildings it is a serious threat for built property. This is even more the case in present times of reduced investment budgets and relatively small numbers of new-built dwellings, which increase the relative importance of the existing housing stock. Knowledge about the prevention, the diagnosis and the treatment of obsolescence is therefore of growing importance. In previous publications the available knowledge about obsolescence was combined in a conceptual model. The model was tested in a series of varied case studies and combined with the findings from an inventory of tools to detect and measure different kinds of obsolescence.
As the next step in the research, the application of the model is tested. The main objective of this step is the elaboration of the model into a practical instrument to identify and to measure obsolescence. The main research question is to what extent the model can be applied to assess building obsolescence, or more explicitly, the ‘level’ of obsolescence of identifiable parts of the housing stock, i.e. estates or product-market combinations. This question has two sides:
- the input side: are the necessary data available?
- the output side: is it possible to interpret that what the instrument shows as a level of obsolescence?
For practical reasons, the study was based on interviews and case data of two large Dutch housing associations in varying housing market conditions as further described in section 3.
Due to delayed data provision - we received the data only shortly before the submission - this paper can be characterized as ‘work in progress’.
The paper comprises four sections:
- A short description of the model (section 2)
- The selection and set-up of the conducted case studies (section 3) - The (results of the) case studies (section 3)
- Conclusions and perspectives for further research (section 4)
2. The model
Obsolescence is defined as the declining performance of buildings (Miles et al., 2007) resulting in the end of what Awano (2006) calls the service life of buildings. The word ‘declining’ in this definition implies that obsolescence is not, or at least not primarily, regarded as a situation, but as a process, more in particular a process of decline. Performance is defined as the extent to which buildings meet requirements and preferences. These requirements and preferences can be incorporated in buildings regulations but they can also be expressed on the housing market. Thomsen and van der Flier (2011) distinguish four types of obsolescence based on two dimensions:
- physical – behavioural - endogenous – exogenous
Physical aspects regard the physical characteristics of the building. Obsolescence can find expression in these characteristics, e.g. in defects. Behavioural aspects regard the behaviour regarding the building. Obsolescence can also find expression in the behaviour of users and owners regarding the building, e.g. misuse or declining appreciation on the housing market. Endogenous aspects regard processes related to the building itself. Changing behaviour regarding the building can be caused by changing physical characteristics, e.g. worn-out utilities. Exogenous aspects regard external processes, e.g. an earthquake that impact on the physical characteristics of the building or on the behaviour regarding the building. Combined a basic model can be made with four types of obsolescence (figure 1).
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Figure 1, Basic conceptual model of obsolescence (revised)
Thomsen and van der Flier (2012 and 2013) tested the applicability of the model in the practice of housing and property management in the Netherlands. They also made an inventory of available Dutch tools that yield information about the different aspects of obsolescence. The results are summarized in an elaborated model of obsolescence (figure 2; see Thomsen and van der Flier, 2014).
Figure 2, Elaborated model of obsolescence and data sources (revised)
3. The selection and set-up of the case studies
The objective of the research that is reported in this paper is to develop an instrument to assess for parts of the housing stock, e.g. estates or product-market combinations, the ‘level’ of obsolescence. To find out to what extent it is possible to realize the objective two case studies have been conducted in the Dutch social rented housing stock. The reasons to start the investigation in the Dutch social rented sector are pragmatic: the nearby availability, the expected ample availability of the required
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information about housing estates in different levels of obsolescence and varying market conditions, the professionalism of the proprietors and their willingness to cooperate. Considering the explorative stage of the research, the specific character of the Dutch housing stock, in particular the relative young age, is not considered as an obstacle. Two large housing associations in the Amsterdam and Rotterdam region (abbreviated here as A and R)were willing to cooperate and to provide the data identified in the model.
Since it is expected that variations in obsolescence are related to housing market conditions and building type (Thomsen and van der Flier, 2013), eight estates from the stock of each housing association, built before 1995 and answering to this variety have been selected. The selection is not representative for the total stock of the housing association. The selected estates are:
- Four estates in a relatively tight part of the regional housing market : an estate dominated by single family dwellings, an estate dominated by 1-4 storey apartment blocks with and without elevator and an estate dominated by blocks of 5 or more storeys.
- Four estates in a relatively loose part of the housing market that the association is working in: an estate dominated by single family dwellings, an estate dominated by 1-4 storey apartment blocks with and without elevator and an estate dominated by blocks of 5 or more storeys. Eight estates of two associations result in sixteen estates in total. From these sixteen estates general and specific information about the four types of obsolescence identified in the model was collected:
- General information: name, address and location (=I); number of dwellings (=II); building year (=III); building type (=IV) and rent level (=V)
- Specific information about the four types of obsolescence from two periods, i.e. 2005-2007 and 2010-2012:
• average technical condition (condition measuring score) (=A1); average housing quality (number of WWS points)1 (=A2); average energy efficiency (EPBD label) (=A3); percentage of energy-inefficient dwellings (with low EPBD label E, F or G) (=A4)
• class of environmental nuisance (=B1); presence of disturbing functions in the neighbourhood (=B2)
• average length of occupation (=C1); turnover rate (=C2); vacancy rate (=C3); response rate (the average number of responses when a vacant dwelling is offered to new tenants) (=C4); acceptance rate (average number of tenants a vacant dwelling has been offered to before re-let) (=C5)
• liveability score (=D1); average property value (WOZ) 2 (=D2) and average quality/value ratio (WOZ)property value per WWS point) (=D3).
1) There are two ways to assess the ‘level of obsolescence’ of estates:by comparing the data of each estate from the 2005-2007 period with the data of the 2010-2012 period. The differences are interpreted as changing levels of obsolescence. In this way obsolescence is understood as a process. Table 1 shows this way of comparing for one type of dwellings in a tight housing market.
2) by comparing the data of each estate in one period of time with relevant local or national references from that period. In this way obsolescence is understood as (relative) state or condition of an estate.
1 All rental homes in the Netherlands are assessed according to the principles of the Housing Valuation System (“Woningwaarderingsstelsel” - WWS), on the basis of which a number of points is given. This number of points determines the maximum rent that the landlord is allowed to ask from the tenant.
2 The WOZ property value, in which WOZ stands for the Act on Valuation of Real Estate (“Wet waardering onroerende zaken”), is assessed periodically for every home, either rental or owner-occupied. This is done by the government for mainly fiscal reasons.
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Table 1 Example of data regarding obsolescence for a given estate Complex 1 (A): Single family dwellings; tight housing market
A Physical Building Obsolescence B Physical Location Obsolescence
Indicator Score 2007 Score 2013 ∆ : ↑,↓, 0, n.a. Indicator Score 2007 Score 2013 ∆: ↑,↓, 0, n.a.
A1 Cond. assessment(range 1-6) A2 WWS points (N) A3 Energy label (cl) A4 Labels E+F+G (%) n.a. 150.3 n.a. n.a. 2.67 144.8 D 31 n.a. ↓ n.a. n.a. B1 Env. nuiss.(cl) B2 Dist.funct.(Y/N)
(no recent zoning plan)
N
(no recent zoning plan)
N
C Behavioral Building Obsolescence D Behavioral Location Obsolescence
Indicator Score 2007 Score 2013 ∆: ↑,↓, 0, n.a. Indicator Score 2007 Score 2013 ∆: ↑,↓, 0, n.a.
C1 Occ. length (year) C2 Turnover rate (%) C3 Vacancy rate (%) C4 Response (N) C5 Acceptance rate (N) 22.7 6.1 0.0 n.a. n.a. 23.0 7.3 0.0 72 n.a. ↑, ↑ ↑,↓, 0 n.a. n.a. D1 Liveability (cl) D2 Property value (€) D3 Property value per WWS point (€) moderately positive 195,839 1,303 moderately positive 175,113 1,209 0 ↓ ↓
6 4. The results of the case studies
The results of the search for data are rather disappointing. It turned out to be impossible for the two housing associations to produce time-series of data on the variables mentioned. Even comparing two periods of time is hardly possible. Table 2 gives an overview of the type of data available at A. The information systems of the housing association can provide data from the 2012-3 period. Older data, from the 2005-7 period, are not readily available and would have to be collected from the archives of the respective housing associations or by interviews with the people responsible for the management of the estates at that time, both very labour-intensive and time-consuming activities. Since the data were received only recently it was impossible to do this for this paper.
The shortage of data means that it is impossible to assess the level of obsolescence by comparing data from two periods of time.
Table 2 Data available at A (about single family estate in tight market)
North Randstad Estate
Single family dwellings - tight market 2007 2013
A1 Condition assessment (range 1-6) n.a. 1.8
A2 WWS points (N.) n.a. 143
A3 Average energy label (cl) n.a. E
A4 Energy label E+F+G (%) n.a.. 99.5
B1 Environmental nuisance (cl) n.a. n.a.
B2 Disturbing functions (Y/N) No No
C1 Length of occupation (year) n.a. n.a.
C2 Turnover (%) n.a. 6.0
C3 Vacancy rate (%) n.a. 0.0
C4 Response rate (N) n.a. 169
C5 Acceptance (N) n.a. n.a.
D1 Liveability (cl) n.a. 5
D2 WOZ value (€) n.a. 185,000
The second way to assess the level of obsolescence is by comparing the data of each estate in one period of time with relevant local or national references from that period. For a number of variables relevant references for 2013 have been collected. Table 3 shows the references found.
Table 3 Data available at A and references (about single family estate in tight market)
North Randstad Estate References
Single family dwellings - tight market 2007 2013 2007 2013
A1 Condition assessment (range 1-6) n.a. 1.8
A2 WWS points (N.) n.a. 143 129.1 131.3
A3 Average energy label (cl) n.a. E D
A4 Energy label E+F+G (%) n.a. 99.5 35
B1 Environmental nuisance (cl) n.a. n.a.
B2 Disturbing functions (Y/N) No No
C1 Length of occupation (year) n.a. n.a.
C2 Turnover (%) n.a. 6.0 8.7 8.2
C3 Vacancy rate (%) n.a. 0.0 1.1 1.5
C4 Response rate (N) n.a. 169
C5 Acceptance rate (N) n.a. n.a.
D1 Liveability (cl) n.a. 5
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Table 3 shows that relevant references have been obtained for a limited number of variables. For condition measuring (A1) no references are known. The environmental variables, B1and B2 are by definition local. For regular management data (C1-5) some references are available. Data about liveability (D1) can be obtained but further research is needed to collect them.
The ratios for all the estates of both associations have been compiled in tables 4 and 5.
To compare the ratios of estate data and reference data so-called radar graphs, e.g. figure 3, can be used or a regular table. For this reason the ratios have been scaled using a 10 point scale, ranging from 1 (high level of obsolescnece) via 5 (average level) to 10 (low level).
Figure3 Example of radar graph of building performance
Table 4 Ratios A estates
sin g le fa m il y 1 -4 s to re y w it h o u t e le v a to r 1 -4 s to re y w it h e le v a to r > 5 s to re y s si n g le fa m il y 1 -4 s to re y w it h o u t e le v a to r 1 -4 s to re y w it h l if t > 5 s to re y s
Market type tight tight tight tight loose loose loose loose
A1 Condition measuring (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
A2 WWS (pt.) 5,45 5,29 4,07 4,95 6,44 4,99 5,86 5,06
A3 Energy label (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
A4 Label E+F+G (%) -4,21 6,28 8,55 10,00 9,77 10,00 -3,78 8,73 B1 Environmental nuisance (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 B2 Disturbing functions (Y/N) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 C1 Length of occupation (year) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
C2 Turn over (%) 6,34 6,34 5,98 6,16 5,37 5,61 6,65 6,89 C3 Vacancy (%) 10,00 10,00 2,07 10,00 10,00 -13,33 10,00 -4,73 C4 Response (N) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 C5 Acceptance (N) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 D1 Liveability (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 D2 WOZ value (€) NA NA NA NA NA NA NA NA
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Table 5 Ratios R estates
sin g le fa m il y 1 -4 s to re y w it h o u t e le v a to r 1 -4 s to re y w it h e le v a to r > 5 s to re y s si n g le fa m il y 1 -4 s to re y w it h o u t e le v a to r 1 -4 s to re y w it h l if t > 5 s to re y s
Market type tight tight tight tight loose loose loose loose
A1 Condition measuring (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
A2 WWS (pt.) 5,51 3,73 4,94 4,49 6,28 5,02 4,55 4,69
A3 Energy label (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
A4 Label E+F+G (%) 5,62 4,29 8,91 3,51 10,0 10,0 10,00 8,73
B1 Environmental nuisance (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 B2 Disturbing functions (Y/N) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 C1 Length of occupation (year) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00
C2 Turn over (%) 5,55 2,74 5,79 8,11 5,67 5,55 2,13 8,66 C3 Vacancy (%) 10,0 3,00 10,0 10,0 -0,67 4,67 10,00 10,00 C4 Response (N) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 C5 Acceptance (N) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 D1 Liveability (cl) 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 D2 WOZ value (€) 4,49 4,49 4,19 4,31 4,43 4,50 4,54 4,59
D3 WOZ value per WWS-point
(€) 4,14 3,84 3,20 3,68 2,81 3,07 2,75 3,19
The cases have been selected by dwelling type and housing market condition. The selection was not representative. Keeping that in mind a few results from tables 4 and 5 can be highlighted:
- There is no systematic variation in scores of the four dwelling types
- There is some variation in scores in different housing market conditions. In tight markets the vacancy rates are lower and the property values per WWS point are higher.
5. Conclusions and next steps
In this paper an answer was sought for the question to what extent the model of obsolescence can be used as instrument to determine levels of obsolescence. The answer to this question has two sides, namely the input side: - are the necessary data available? - and the output side: - is it possible to interpret the results as level of opbsolescence?). The answers found in this stage bear a preliminary character because the input side of the model is still in development and because the data came available only shortly before the submission of this paper.
Regarding the input, i.e. the availability of data, the results are rather disappointing. Time series of data - required given the definition of obsolescence as a process of decline - turned out to be not available in both cases. As a result, longditudinal analyses of obsolescence as a process are not possible and the research question can only partly be answered. Most of the collected data are nevertheless sufficient to indicate a level of obsolescence, albeit that data for exogenous physical obsolescence (quadrant B) is not systematically collected nor readily available.Regarding the output, i.e. the level of obsolescence, the results show that it is possible to produce ratios of estate data and local and national references. However, more cases and better references are needed to be able to find patterns that can be interpreted as level of obsolescence.
As a more general conclusion, the results in this stage underline the relevance of obsolescence for the practise of housing management, the practical usabilitity of the conceptual model and the feasibility of a practical tools based on the model to analyse and evaluate ageing processes. The further
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process with a wide range of interrelated cause-effect relations, the knowledge and inventory of these relations and its indiactors will need further time-consuming effords.
A next step in the research in this respect can be to find out if the availability of data is the same for other housing associations. Another step is collecting the data and especially the older data from the 2005-2007 period. A third step is carrying out technical inspections in a sample of dwellings. it may be clear, however, that the latter two steps require very labour-intensive fieldwork, which cannot be realised on the short term.
References
Awano, H. (2006). Towards Sustainable Use of the Building Stock. Urban Policy Development Workshop (OECD/IEA, Ed.) OECD, Paris.
Miles, M. E., Berens, G. L., and Weiss, M. A. (2007). Real Estate Development: Principles and Process. 3 ed. (U. L. Institute, Ed.) Urban Land Institute, Washington, DC.
Thomsen, A.F. & Flier, C.L. van der (2011). Understanding obsolescence: a conceptual model for buildings. Building Research and Information 39(4), 352-362.
Thomsen, A.F. & Flier, C.L. van der (2012). Housing obsolescence; a pilot study. Paper for ENHR conference 2012. Lillehammer.
Thomsen, A.F. & Flier, C.L. van der (2013). Housing obsolescence in practice; model implementation. Paper for ENHR conference 2013. Tarragona.
Thomsen, A.F. & Flier, C.L. van der (2014). Analysing obsolescence, an elaborated model for residential buildings.. Building Research and Information. (forthcoming).
