Results

Seventy (70) out of a total of 110 questionnaires administered to the real estate valuers were received. This constituted 63.64% of the questionnaires that were administered. This is more than the response rate recorded from comparable studies such as Adair et al. (1996) (56%). However, for a few of the received questionnaires, some of the questions were not answered. These were taken into account in the data analyses.

Most of the respondents had less than 15 years of professional experience (30% were below 5 years of experience, 24.29% within 5-9 years, 15.71% within 10-14 years, and 18.57% above 25 years) (Figure 2.1). This result could be attributed to the increasing numbers of student intake on both the B.Sc. (Hons) Land Economy and Real Estate programmes at the KNUST over the last ten years or more compared to the initial years when the Land Economy program was introduced. Since graduates from these programmes have been the main source of professional membership for the Valuation and Estate Surveying (VES) division of the GhIS, it can be inferred that any change in student intake on the programmes would affect the membership state of that division. However, regarding the nature of practice of the respondents, 45.71%, 25.71%, and 28.57% of

Respondents’ years of experience

FIGURE 2.1 Respondents’ years of experience.

Nature of respondents’ professional practice

FIGURE 2.2 Nature of respondents’ professional practice.

the respondents were in private practice, worked for government/public, and private organizations respectively (Figure 2.2).

Extent of Variation in Valuations

Tables 2.1 and 2.2 summarize the results on the extent of variation in the market value estimates received from the respondents on the three-bedroom residential property that constituted the subject matter of the valuations. Overall, the results indicate quite a level of variation, with a coefficient of variation of 0.63 (63%). The results also show that only 2.86%, 8.57%, and 14.29% of the valuations fell with ±5%, 10%, and 20% of the median for the reported value estimates respectively (Table 2.2). Further, these results show a much higher variation compared to Adair et al. (1996) in the UK, which established that the majority (80%) of the sampled valuations (n = 446) produced a variation from the mean of <20%, and Hansz and Diaz (2001) in the US, which produced a coefficient of variation of 0.098 (9.8%). This further emphasizes the extent of variation in the valuations.

A number of plausible reasons could be attributed to the high level of variability in the value estimates that were returned by the respondents. First, the subject matter of the valuations:

TABLE 2.1

Summary Statistics of the Reported Market Value Estimates

Statistic

Values

Minimum

90,700

Maximum

3.364.480

Mean

1.247,379

Median

1.134.650

Standard Deviation

784,428

Coefficient of Variation

0.63

TABLE 2.2

Percentage Mean and Median Variation in Valuations

Percentage Variation

Mean

Median

<5

2.86%

2.86%

<10

7.14%

8.57%

<20

21.43%

14.29%

<30

34.29%

31.43%

<40

42.86%

42.86%

<50

57.14%

57.14%

<60

60.00%

60.00%

<70

72.86%

70.00%

<80

78.57%

71.43%

<90

85.71%

84.29%

a government leasehold interest in a residential property with an unexpired term of ten years may have appeared quite complex to the respondents. This is because in practice, valuations of such properties are often valued as a 50-year interest based on the assumption that government will renew the lease, although renewal is usually undertaken at a substantial cost, and it is not guaranteed. Secondly, the varying levels of experience of the sampled valuers (refer to the earlier discussion under the main subsection) may have resulted in different interpretation of the property market and the value estimates. Thirdly, the use of different methods of valuation to arrive at market value estimates could have accounted for the high variability in the valuations. The results show that seven different approaches (sales comparison (35.71%), replacement cost (38.57%), investment (15.71%), sales comparison and replacement cost (4.29%), sales comparison and replacement cost and investment (2.86%), sales comparison and investment (1.4%), and investment and replacement cost (1.4%)) were used by the respondents to produce the market value estimates. While this may reflect inadequate standardization in valuation practice, which could be corroborated by the numerous reasons reported by the respondents for the choice of a particular approach, it also mirrors the property market data challenge to valuation practitioners. This is particularly so, given that the valued hypothetical residential property was located in one of the prime and choicest areas of the case study country where the property market is very articulate, and that based on valuation theory, the market approach (sales comparison) should have been the preferred method of valuation.

Property Market Data Sources

Evaluation of the frequency of use of property market data sources by the respondents and their reliability was undertaken with Equation (2.2). A 5-point Likert scale was used to elicit the required responses. Results from the evaluation are reported in Tables 2.3 and 2.4.

In broad terms, the results demonstrate that valuers want to use property market data sources they perceive to be reliable. However, relying on professional colleagues for property market data was the most used property market data source (Agrl5 = 0.939) compared to the media, which is the least used (Agrl5 = 0.548). Valuation practitioners’ own database was the second most used property market data source (Agrl5 = 0.826). This was followed by public institutions (Agrl5 = 0.797), estate developers (Agrl5 = 0.784), estate agents (Agrl5 = 0.717), and property owners (Agrl5 = 0.636), in that order. Ease of access to property market data, the need for valuation practitioners to check the reliability of their own databases, and the reliability of the property market data sources are possible reasons for the results. However, apart from the media as a property market data source whose usage corresponded to how the respondents rated its reliability, there were variations in how the respondents rated the reliability of the other property market data sources, compared with their frequency of use.

TABLE 2.3

Extent of Use of Property Market Data Sources

Source

N

Frequencies (%)

Min

Max

Mean

Median

Mode

AgrIS

1

2

3

4

5

Property Owner

70

2.86

21.43

37.14

18.57

20.00

1

5

3.31

3

3

0.636

Estate Agent

69

1.45

7.25

34.78

39.13

17.39

1

5

3.64

4

4

0.717

Professional

Colleagues

69

0.00

0.00

8.70

13.04

78.26

3

5

4.70

5

5

0.939

Public Institutions

70

0.00

10.00

20.00

27.14

42.86

2

5

4.03

4

5

0.797

Estate Developers

69

1.45

10.14

14.49

37.68

36.23

1

5

3.97

4

4

0.784

Media

70

12.86

24.29

25.71

27.14

10.00

1

5

2.97

3

4

0.548

Own Database

66

3.03

6.06

10.61

30.30

50.00

1

5

4.18

4.5

5

0.826

1 = Do not use at all, 2 = Rarely, 3 = Quite often, 4 = Often, 5 = Very Often

TABLE 2.4

Reliability of Property Market Data Sources

Source

N

Frequencies (%)

Min

Max

Mean

Median

Mode

Agrl5

1

2

3

4

5

Property Owner

70

2.86

10.00

42.86

28.57

15.71

1

5

3.44

3

3

0.671

Estate Agent

69

1.45

11.59

42.03

39.13

5.80

1

5

3.36

3

3

0.657

Professional

Colleagues

70

0.00

0.00

7.14

42.86

50.00

3

5

4.43

4.5

5

0.888

Public Institutions

70

0.00

5.71

25.71

31.43

37.14

2

5

4.00

4

5

0.794

Estate Developers

68

1.47

4.41

19.12

38.24

36.76

1

5

4.04

4

4

0.802

Media

67

7.46

22.39

50.75

17.91

1.49

1

5

2.84

3

3

0.528

Own Database

65

0.00

0.00

3.08

32.31

64.62

3

5

4.62

5

5

0.925

1 = Very unreliable, 2 = Unreliable, 3 = Quite reliable, 4 = Reliable, 5 = Very reliable

Although obtaining property market data from professional colleagues was the most often used data source, it was not perceived as the most reliable (Agrl5 = 0.888). On the contrary, practitioners’ own database (Agrl5 = 0.925) was rated as the most reliable, even though it was not the most often used. Nevertheless, obtaining property market data from professional colleagues was perceived as more reliable than the other property market data sources. A possible reason for this finding could be the confidence that professionals have in the property market data collection and management capabilities of their colleagues. Further, obtaining property market data from real estate developers was rated as the next most reliable data source (Agrl5 = 0.802), followed by public institutions (Agrl5 = 0.794), property owners (Agrl5 = 0.671), and estate agents (Agrl5 = 0.657). The finding for real estate developers may be due to their ability to often provide current property market data. Inadequate record-keeping, and the tendency for provision of out-of-date data and the bureaucratic process associated with the provision of property market data may explain respondents’ lack of confidence in the reliability of public institutions as a source of property market data. Also, the practice of not disclosing details of real estate transactions by property owners, and the poor data collection and documentation on real estate transactions by real estate agents are plausible explanations for the relatively low level of reliability respondents assigned to these sources.

 
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