African Real Estate Research - UCT Open Access Journals

The use of heuristics in the human decision-making process is the result of limitations of the human brain, especially when confronted with a complex task environment (Hardin, 1997; Havard, 2001). As noted above, the use of heuristics is due to the limited capabilities of the human brain.

Table 2: Characteristics of Information Processing System S/N Characteristic Implication

Methodology

This included a survey of appraisers' perceptions of the entire appraisal process using the replacement cost method. However, some of the questions have been formulated after consultation with experienced appraisers in the field.

Results

A small proportion (9%) of respondents admitted that they formed value predictions due to customer influence. To test RH, respondents were asked three questions based on property inspection and description stage of the valuation process.

Table 5: Availability Heuristics

Discussion of the Results

From Table 8 above it can be noted that valuers are more likely to ignore the size of storage room in their valuation than they would for the size of the kitchen or an extra bathroom. The results of the RII mean that valuation surveyors take into account all other characteristics except for the size of the storage room.

Implications of the Findings to Valuation Practice

From the findings there are indications that the behavior of the evaluation interviewer has implications in a number of aspects in the evaluation process.

Conclusion

The influence of appraisers and valuation on the performance of the commercial real estate investment market. The results show that the main component in determining the residential location of tenants is the housing attribute, which consists of the number of bathrooms, the number of toilets and the number of bedrooms.

Introduction

Literature Review

Studies by Kim et al. 2005a), Zondag and Pieters (2005) and Curtis and Montgomery (2006) suggest that regardless of stated preferences and revealed preferences, household decisions consist of two main stages, namely the residential mobility stage and the housing selection stage. Both studies did not attempt to determine the level of contribution of categorized housing characteristics to household RLC.

Figure 1. The Sequence of the Housing Choice Decision-Making Process

The Study Area

A careful look at studies in Africa reveals the importance of socio-cultural characteristics in household RLC, in contrast to studies in continents such as America and Europe. Therefore, it is warranted to investigate the RLC of different household types in different geographical areas.

Methodology

Thus, the 14 housing variables and the sample size of 277 employed in this study are considered sufficient for the factor analysis. The Cronbach alpha coefficient of a scale should be 0.7 or above for the items of the instrument to be considered reliable for analysis (Cronbach Saidu & Oyewobi, 2018).

Table 2: Questionnaire Distribution to Tenants in the Study Area Neighbourhood

Results and Discussion

The results of the principal component analysis are shown in Table 8 and the scree plot (Figure 3). Based on the content of the variables, we named the component neighborhood and housing attributes (3rd component).

Table 5: Reliability Test

Conclusion and Recommendations

Study of the factors affecting choice of residential location of the garment workers in Mirpur, Dhaka City. These differences in residential rental values ​​(RRVs) arise due to the predominantly informal nature of the rental housing market in Ghana.

Theoretical Framework: Explanatory Variables that Determine Rental Value – Evidence from Extant Literature

Which is defined as "the estimated amount for which a share in real estate is to be leased on the valuation date between a willing lessor and a willing lessee on suitable lease terms in an arm's length transaction, after proper marketing and where the parties had each acted. knowingly, carefully and without coercion" (International Valuation Standards Council, 2017: p.21). However, there appears to be some convergence with variables used in the developed country context (Malpezzi, 2002; Sirmans et al., 2005) (ie property age, floor area, number of floors, number of bathrooms and bedrooms).

Figure 1: Ripple Effect to Induce more Quality Housing Theory (adapted from Tse 2002; Ozanne & Thibodeau 1983)

The Nature of the Residential Rental Housing Market in Ghana – An Overview

The informal market is part of the urban structure due to the phenomenon of urban sprawl. The informal market is used by the majority of the population for housing supply due to low income.

Figure 2: Residential Rental Accommodation Types in Ghana

Methodology 1 Survey Design

Both closed and open questions were included in the questionnaire after the pilot test was undertaken to identify potential problems and the feasibility of the following research methodology. The types of data collected during the survey included the category of respondents, years of contact with the real estate market, a ranking of variables that determine rental values ​​and a brief description of the rental market in Accra.

Figure 4: Map of the Greater Accra Region

Determinants of Residential Rental Values: Evidence from Key Stakeholder Survey

To conclude, in Table 3, it is realized that most of the variables identified throughout the literature and presented here are considered significant and can have a positive coefficient sign when modeled. In terms of further research on this phenomenon, other researchers are encouraged to consider their inclusion and test how stakeholders would rank these variables in terms of the effect on rental value.

Table 2: Cronbach Alpha Scores for Variables Overall Cronbach alpha reliability 0.963

Conclusions and Implications

Acknowledgement

Hedonic prices and the demand for housing features in a Third World city: The case of Ibadan, Nigeria. Slum Real Estate: The Low-Quality High-Price Puzzle in Nairobi's Slum Rental Market and Its Implications for Theory and Practice.

Appendix

This paper examined the relationship between Nigerian Real Estate Investment Trusts (N-REITs) and Money Market Indicators (MMIs) which consist of: Currency in Circulation (CIC), Broad Money Supply (BMS), Corporate Private Sector (CPS), First lending rate (PLR) and treasury bill rate (TBR). The money market is central to the debt and equity financing of capital projects such as Real Estate Investment Trusts (REITs) under the country's monetary policy arrangements.

Global Outlook on REITs

However, Oni, Emoh and Ijasan (2011) found that interbank rate, monetary policy rate (MPR) and inflation are the main main components affecting real estate investment. As a potential destination for real estate investors, the Nigerian real estate market was ranked 40th in terms of size.

Table 1: Continental Outlook of Global REITs and their Ranking

Literature Review

In Nigeria, the study of the effect of monetary policy variables focuses on economic growth in terms of GDP. Given two stationary time series X = {X(t)}tєZ, and Y = {Y(t)}tєZ with the following information sets:. i) I*(t), the set of all information in the universe up to time t, and (ii) I*−X(t), the set of all information in the universe excluding X up to time t.

Results

The cointegration test shows the forms (short or long) of the relationship that exists between the variables. This means that more values ​​in the SKR and TBR data sets are lower than their mean value.

Table 3: Summary of Tests and Analysis (Normality, ADF, VAR &

Discussion of Findings

For short-term analysis, MMIs such as TBR, OUR and CPS (with p<0.05 Granger) cause N-REIT dividend returns, while CIC and BMS (with p>0.055) do not cause N-REIT dividend returns in the VAR model. The VECM model for long-term analysis (Tables 3) suggests that only OUR Granger can cause N-REIT dividend returns, as it is the only indicator with a significant p-value (0.03) in a long-term relationship.

E Stats

Data Analysis and Discussion

From Table 3, it was found that the inaccuracy of the valuations was most influenced by the assumptions of the appraisers. This shows that the higher the degree of valuation inaccuracy, the lower the performance of the investment.

Table 1 depicts the proportion of valuations across the error distribution table.

Conclusion and Recommendations

However, the result indicates that the effect of inaccurate valuations on the performance of commercial real estate investments is statistically insignificant. This study then concluded that taking into account the high degree of inaccuracy of valuation in Akure, valuers should be very careful in making assumptions and using market indices for their valuations due to the risks that may arise.

Acknowledgment

Purpose – The purpose of the study is to determine the effectiveness of a decision support tool in reducing bias in real estate valuation. It occurs when the appraiser or the valuation (i.e. the techniques, processes, systems, etc. used by the appraiser) exhibits random or systematic errors.

Figure 1: Appraisal bias according to Yiu et al. (2006), as depicted in Lausberg and Dust (2017, p

Analysis of Data and Discussion 1. Data collection

Considering the unbalanced and small sample size of the data, the modified robust Brown-Forsythe Levene type test from the median with adjusted zero correction method was applied (Table 4). The same measures of variation and significance level tests were used to test the effectiveness of the DSS version within the subsamples of experts and students.

Table 1 shows that 42 experts and 46 novices were considered valid data sets.

Conclusions

However, unlike the German study that showed some evidence of the benefits of the DSS tool at the 1% significance level, this study could not support similar results. There was also evidence for the anchoring-adaptation heuristic, and it was observed that the automated system can help counteract the unwanted cognitive mechanism generated by inexperienced decision makers.

Implications for Valuation Practice and Research

When comparing tools, descriptive statistics showed that spread was more common in the STD version than in the DSS version. Fourth, developers of evaluation software should engage in further development of their products in the direction of decision support.

Acknowledgments

Appendix A – Assessment Tools Home Page, for both Standard Programs (STD) and Decision Support Programs (DSS). Appendix B – Third page of assessment tools for both Standard Programs (STD) and Decision Support Programs (DSS).

Study Area

Considerable research has been done in different parts of the world to address land use change using GIS (Schmidt & Kedir, 2009; Kharel, 2010; Kumar & Kumar, 2016). The specific objectives of the study are to analyze the pattern and rate of land use change in and around Osogbo using multi-temporal imagery and determine the impact of the observed changes on agricultural land use in the study area.

Figure 1: Osun State in the Context of Nigeria.

Materials and Methods

The percentage change is used to determine the trend of change and is calculated by dividing observed changes by the sum of the changes. The second task was the use of composite growth rate (R) in measuring the rate of change in land use types.

Table 1: Summary of Landsat Images Acquired for the Research Date Satellite

Results and Discussion

The main reason for urban expansion of the study area is the relocation of the state capital between 1986 and 2002. All of the above caused land use and land cover change which resulted in a loss of agricultural land in the study area.

Table 2: LULC Distribution between 1986 and 2018 Land Use Type

Policy Implications

In order to ensure sustainable development and food security in the area in question, it is necessary to balance urbanization and appropriate use of agricultural land. The study therefore recommends that the government should include agricultural land in urban land use planning to effectively manage and protect the shrinking agricultural space.