A number of initiatives are underway for digitalizing real estate transaction processes. Public and private sector bodies are working to automate information retrieval and processing of the financial, ordinance  and  fiscal aspects of such transactions. Other initiatives, such as ours,  are targeted toward helping  stakeholders directly involved in selling and buying  real estate. We present the results from a set of  group sessions, where the focus was on improving the presentation of salient information to sellers and buyers of property. Based on an earlier conceptualization of perceived  information difficulties, we   elicited user stories  for facilitating a better generation, provision and consumption of relevant information for the  residential real estate transaction process. A total of ten services were aggregated from the user stories. We then asked a set of stakeholders to rate the effect of the services on functional objectives; i.e., on how they will affect the transaction process.  We asked stakeholders at the managerial level to rate the functional objectives on strategic objectives. Combining the two sets of ratings, one obtains a rating of perceived benefit for the services, which can help in prioritzing which services to start developing first. In the outset, real estate transactions involve stakeholders with opposing interests. We conclude that multi-stakeholder group sessions can help generate services that serve these conflicting interests on a common ground. 

Context: The mean magnitude of relative error (MMRE) is an error measure frequently used to evaluate and compare the estimation performance of prediction models and software professionals.

Objective: This paper examines conditions for proper use of MMRE in effort estimation contexts.

Method: We apply research on scoring functions to identify the type of estimates that minimizes the expected value of the MMRE.

Results: We show that the MMRE is a proper error measure for estimates of the most likely (mode) effort, but not for estimates of the median or mean effort, provided that the effort usage is approximately log-normally distributed, which we argue is a reasonable assumption in many software development contexts. The relevance of the findings is demonstrated on real-world software development data.

Conclusion: MMRE is not a proper measure of the accuracy of estimates of the median or mean effort, but may be used for the accuracy evaluation of estimates of most likely effort.

In this paper, we propose improvements in how estimation bias, e.g., the tendency towards under-estimating the effort, is measured. The proposed approach emphasizes the need to know what the estimates are meant to represent, i.e., the type of estimate we evaluate and the need for a match between the type of estimate given and the bias measure used. We show that even perfect estimates of the mean effort will not lead to an expectation of zero estimation bias when applying the frequently used bias measure: (actual effort – estimated effort)/actual effort. This measure will instead reward under-estimates of the mean effort. We also provide examples of bias measures that match estimates of the mean and the median effort, and argue that there are, in general, no practical bias measures for estimates of the most likely effort. The paper concludes with implications for the evaluation of bias of software development effort estimates.