When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you're measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for-or undertake-other research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the updated confidential information for iBuyers, to be distributed for a second round of play. It is one of eight role documents to be assigned to students in the simulation.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the updated confidential information for homebuyers, to be distributed for a second round of play. It is one of eight role documents to be assigned to students in the simulation.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for iBuyer 3, and is one of eight role documents to be assigned to students in the simulation.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for iBuyer 2, and is one of eight role documents to be assigned to students in the simulation.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for iBuyer 1, and is one of eight role documents to be assigned to students in the simulation.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for Homebuyer 3, and is one of eight role documents to be assigned to students in the simulation.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for Homebuyer 2, and is one of eight role documents to be assigned to students in the simulation.
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for Homebuyer 1, and is one of eight role documents to be assigned to students in the simulation.
This note provides an overview of real estate iBuying, or instant buying, a business model that involves buying homes and then reselling them at a profit. Introduced in the mid-2010s, iBuying streamlined the process of selling a home by offering instant, all-cash offers to sellers. This note includes context on the traditional home buying and selling processes, the impact of property technology (PropTech), the iBuying process and unit economics, and the iBuying market landscape (e.g., top players and geographic markets).
Big tech companies such as Google and Booking.com are running tens of thousands of experiments annually, and other businesses are following their lead. Here's how to use experiments in your company to better evaluate customer offerings and policies, test new ideas and innovations, develop and refine decision frameworks, and establish reliable facts in uncertain conditions and ambiguous situations.
Facing mounting criticism and evidence of widespread racial discrimination on the platform, apartment rental platform Airbnb needed to decide a path forward. For years, Airbnb had given hosts extensive discretion about whether to reject a guest after seeing little more than a name and a picture, believing this was the best way for the company to build trust. While Airbnb ran thousands of experiments per year looking at ways to grow the user base and short-run profit, they failed to track or account for the possibility of discrimination. Should they become more proactive about identifying discrimination on the platform? Should they change the design of the platform to reduce discrimination? If so, how would they decide whether the changes were successful?
Online reviews are transforming the way consumers choose products and services of all sorts. We turn to TripAdvisor to plan a vacation, Zocdoc to find a doctor, and Yelp to choose a new restaurant. Reviews can create value for buyers and sellers alike, but only if they attain a critical level of quantity and quality. The authors describe principles for setting the incentives, design choices, and rules that help review platforms thrive. To address a shortage of reviews, companies can seed them by hiring reviewers or drawing reviews from other platforms; offer incentives; or pool products. To address selection bias, they can require reviews, allow private comments, and design prompts carefully. To combat fraudulent and strategic reviews, they can set rules for reviewers and call in moderators--whether employees, the community, or algorithms.
This case explores how neuroscientist Mariam Chammat helped set up the first behavioral insights team at the center of the French government, and encouraged French administrations to innovate and create policy initiatives based on psychological theories of influence and persuasion. Students are asked to assess 35 projects ripe for behavioral intervention and pick the winning proposals.