iBuyer’s Use of PropTech to Make Large-Scale Cash Offers The expansion of iBuyer’s use of PropTech to major housing markets raises a series of questions for both buyers and sellers when making instant, all-cash offers. This study uses a sequence of experiments to identify the proper implementation of existing behavioral real estate concepts to improve the iBuying process, a burgeoning area of residential real estate. We find strong evidence of anchoring for all-cash offers in that sellers are nearly twice as likely to transact when they are first presented with the net proceeds offer price (market value minus costs) rather than starting with the higher gross market value offer price. After the sale, seller regret aversion becomes strong when the seller’s house is subsequently sold for 10% or more than the all-cash buyer paid, but regret aversion is mitigated with communication of the improvements made to enhance the selling price. We further find that sellers do not know which all-cash buyer’s Automated Valuation Model (AVM) is the most accurate and are therefore much more influenced by brand awareness than model sophistication. Finally, while the extant literature has examined offer price strategies for home sellers, this is the first investigation of buyer offer price strategies. In stark contrast to selling strategies, pricing strategies do not matter when making an offer to buy. Keywords: PropTech, iBuyer, experiment, regret aversion, pricing strategies. iBuyer’s Use of PropTech to Make Large-Scale Cash Offers Introduction A home typically represents not only a person’s largest financial asset, but usually their most illiquid as well. The residential real estate market is said to be inefficient because it is characterized by high transaction costs, a small number of buyers and sellers, price uncertainty, a lack of transparency, and heterogeneous and indivisible assets. These frictions result in lengthy disposition times and have invited financial innovation known as PropTech. For example, in 2014, all-cash instant buyers, known as iBuyers, entered the market to add efficiency for “motivated sellers,” those who have a strong preference for divesting of their residential real estate holding quickly. Instead of listing the home for sale through a traditional brokerage firm, iBuyers stand ready to make a cash offer on a home, even before the potential seller is ready to list. This willingness to pay is accomplished on a large, ex-ante scale through the implementation of an automated valuation model (AVM). An AVM uses a complex algorithm to keep current the value of all homes in an area by updating their database whenever a home within the system transacts. Since the AVM is live and updates constantly, a homeowner can go to an app or website and view the estimated value of their home at any time. If the homeowner likes the price, she can initiate the all-cash sales process from the comfort and convenience of her mobile device or computer. iBuyer activity gained rapid success in the early years of its deployment, representing nearly 10% of all transactions in some large markets (Seiler and Yang, 2022). Given that iBuyer margins are relatively slim, a critical threat to iBuyers is a constant need to ensure the accuracy of their AVM estimate of home value. Zillow famously overpaid for the homes they purchased and in 2021 exited the iBuying market after losing vast sums of money[footnoteRef:1]. As a result, the industry recognizes that AVM accuracy is critical to iBuyer success. AVMs also works better in rising markets than in declining markets. With the jump in inflation and the dramatic increases in interest rates in 2022 and 2023, housing prices stagnated and began to fall in many parts of the country, causing AVMs to lag behind downward market pressures and further jeopardize iBuyers. In response to the reality of pricing risk error and its financial implications, all-cash buyers tend to operate in markets characterized by homogeneous product, with a high number of transactions, within an average price range. While restrictive, iBuyers have plenty of homes that meet the pricing risk criteria. All-cash buying has become so widespread that even traditional brokerage firms are getting into this space. In fact, the lines between iBuyers and traditional brokerage firms are blurring as both market participants are engaging in each other’s business models. For example, Keller Offers works with Offerpad to provide both an iBuyer experience as well as their traditional brokerage services. Unlike most financial assets, homes represent both an investment and a consumption good. As a consumption good, there is more to the deriving of utility than financial performance. Homes and our surrounding neighborhood are where we raise our children, obtain their education, make friends, and create lifelong memories. As such, residential real estate encourages behavioral considerations well beyond those associated with traditional financial asset decision-making. iBuyers recognize behavioral considerations and have been among the leaders in incorporating behavioral theories into the fabric of their operations. [1: https://www.cnet.com/personal-finance/mortgages/what-happened-at-zillow-how-a-prized-real-estate-site-lost-at-ibuying/ by Alix Langone. Referenced on October 6, 2022.] In this study, we present the results of a series of behavioral experiments conducted on homeowners that reflect the concepts these PropTech firms are applying to the residential real estate market. To preview the conclusions, we find a strong presence of anchoring by sellers when being presented with a cash offer. Anchoring is used in behavioral economics to denote an irrational bias towards an arbitrary benchmark or figure that can skew decision-making, and its effects have been shown to be long-lasting in valuation assessments (Yoon and Fong, 2019). Specifically, we show that current homeowners are nearly twice as likely to accept a cash offer if the bottom-line number is presented as opposed to a top line number reflecting the fair market value of the property, then subtracting off fees and expenses before arriving at the amount the homeowner will receive from the iBuyer. Moreover, cash offers from these firms are less preferred by homeowners who view their home as more of an investment. For these sellers, profit maximization seems to play a greater role as they are willing to wait for a potentially higher offer from the market utilizing a traditional brokerage firm. We further find that sellers start to experience substantial levels of regret around the 10% price discount level, but this remorse can be mitigated if the firm explains that renovations were made to the home before reselling it, which caused the fair market value to increase. All-cash PropTech iBuyer firms are concerned that homeowners may prefer to sell to a family as opposed to a company, but we find absolutely no evidence of this. Moreover, offer price strategy, which has been found to matter when selling a home, is not statistically significant in any of our tests when it comes to making offers to buy. This asymmetric finding has not been previously addressed in the literature. Finally, homeowners are generally unaware of which companies generate accurate AVM estimates, and instead form perceptions based on brand awareness, which is an uninformed approach. Background Since 2014, iBuyers have been making inroads into the housing market historically dominated by traditional real estate brokers. These all-cash buying PropTech firms approach homeowners to either list their homes or accept cash buyouts whereby the company will resell the home just months later, usually after making minor improvements. The degree of the improvement varies from home to home, but generally, the firm seeks to buy and sell in a relatively short window. So simple renovations such as paint and flooring tend to be much more common than a full kitchen and/or bath remodel. To this end, iBuyers have a team of skilled workers ready to get the home in optimal shape from a return-on-investment standpoint. The distinction between iBuyer and real estate brokerage business activities is not always well delineated. We adopt the definition that an iBuyer is simply a real estate company who relies heavily on an automated valuation model (AVM) instead of local market experience to purchase homes in distant locales. iBuying describes a process which we refer to simply as an all-cash offer transaction. Real estate brokerage firms are known to use similar approaches, albeit not typically as their core business strategy, and may depend less on high-frequency, high-speed technology-enabled (i.e., PropTech) transactions. iBuyers and the Role of Trust The emergence of iBuyers provides fertile ground for studying behavioral theories in real estate transactions. Buying or selling a home is a transaction fraught with uncertainty and risk due to information asymmetry and heterogeneity of the asset. Markets that do not involve significant uncertainty in the purchase process are unlikely to require high levels of informed decision-making. Generally, trust has long been recognized as pertinent for facilitating market transactions in the economics literature. Marshall (1975) and Arrow (1974) maintain that trust lowers transaction costs by removing some of the costly and time-consuming barriers that non-trusting business relationships face such as the need for collateral, third-party testimonials, and so forth. However, this notion of trust as a competitive advantage depends on imperfect information as the norm in the marketplace (Ottati, 1994). Hence, where agents in a market differ with regards to their individual reputation and how well they know each other, trust is a more decisive factor than it would be in a setting that conveys complete information on any agent to any other market participant. Where information is imperfect, intermediaries such as real estate agents and iBuyers could play an important role in connecting ultimate buyers and sellers to increase market efficiency. The pivotal role of trust in the shared economy has been established by several empirical studies. Greenwood and Van Buren (2010) and Venkateswaran et. al (2021) indicate that trustworthiness and reputation are closely scrutinized in markets where rogue agents could exploit information asymmetries through deviant buying or selling strategies. This study investigates several aspects from a home seller’s perspective. Our experiments investigate participants’ views about broker value, regret of selling, precision of selling price, decision-making strategies, and the context of iBuyers – homebuyers who use an AVM rather than a traditional broker for estimating the value of a home. Several of the questions and decisions participants make in this experiment relate to their assessment of the value of having a broker and the perceived role of the broker. Costs and Benefits of Intermediation by a Broker Cypher et. al (2018) reveal that brokers can influence transaction pricing as buyers are more strongly influenced by brokers than by market information regarding prices. Bernheim and Meer (2008) measure the effect of real estate brokerage services by the terms and timing of sales and find that the costs associated with hiring a broker exceed the actual benefits received by a wide margin. Elder, Zumpano and Baryla (1999) studied the search process and the role of the broker, finding that brokers serve a useful function in providing intermediation in the housing market and reduce search time by increasing its intensity, leading to a quicker home purchase. In our experiment, homeowners choose whether to sell, and if so, how, creating a situation where participants may experience regret from their choice. Loss aversion, the tendency to prefer avoiding losses to acquiring equivalent gains, is studied through similar experiments by Seiler et. al (2008), Sun and Seiler (2013), and Zillante, Read, and Seiler (2019). Pricing Strategies and Perceptions Seiler et. al (2008) participants purchased an investment property years ago and were slotted into two scenarios: one group was told they had failed to sell at the all-time high after their original purchase, while the second group was not made aware of this potential gain. The first group expressed higher regret than the second. One of the many variables tested in our experiment is the effect of more precise offer prices on participant decisions. Beracha and Seiler (2014), Cardella and Seiler (2016), and Bucchianeri and Minson (2013) investigate this effect in their own experiments. Beracha and Seiler (2014) find that prices just below the rounded dollar amount (e.g., $199,900 vs. $200,000) result in the highest transaction price relative to the actual home value. This remains true even as homebuyers negotiate the ‘just-below’ price lower. Bucchianeri and Minson (2013) also investigate list pricing but based on previous transactions rather than experiments. Their findings suggest a positive relation between listing prices and sale prices, indicating that over-pricing will yield higher final transaction prices and explain that anchoring at the list price is likely the reason for the result. Participant behavioral differences and decision-making strategies lead to varying results in the present experiment. Seiler, Seiler, and Lane (2012) and Swope, Cadigan, and Schmitt (2014) observe bargaining behavior and investor willingness in their papers. Seiler, Seiler, and Lane (2012) find that mental accounting, the different values a person places on the same amount of money, is common amongst study participants. Additionally, they find that people have a greater willingness to sell when investments cross the breakeven threshold, without considering the total costs to sell. Swope, Cadigan and Schmitt (2014) experiment with a behavioral bargaining model that allows decisions to be made by both “sincere” and “strategic” participants. They find that strategic responders behave as expected-payoff maximizers, while sincere responders hold to a minimum-acceptable-offer behavior. Market Effects of iBuying Schemes The nascent literature on iBuying is scant as the concept has only recently received academic attention. Seiler and Yang (2022) were the first to document the practice and the underlying fundamental nature of iBuyers, the markets they operate in, and how they differ from other institutional investors in the residential space. The effects iBuyers have not only on the markets in which they operate, but also the spillover effects they have on nearby markets was also investigated. The authors find that in addition to increasing the demand and therefore the price in the target market, there is a spillover pricing effect into surrounding neighborhoods as prospective homebuyers get priced out of their target market. Buchak et. al (2022) focus on the liquidity iBuyers offer to motivate sellers willing to take a reduction in price in exchange for the convenience and mobility it affords the seller. These authors further document a patience on the part of iBuyers when re-selling by showing they list at a small premium and are willing to carry the home for a greater number of days, even into the off-season, holding out for a higher final sales price. In general, providing more liquidity and increasing residential mobility, is considered an aggregate good as it allows people to move in search of a better life more readily (Oishi and Tsang, 2022). Data Our study was conducted via Amazon’s “Mechanical Turk” (also known as “MTurk”), an online clearinghouse that connects businesses, developers, and academics with “crowd-workers,” who are paid to perform tasks. While any adult may respond to MTurk postings, given the subject matter of our study, we stipulate respondents must be current homeowners living in the United States. The other condition is that participating workers have a past approval rating of at least 95%. To ensure workers are U.S residents, participant IP addresses were cross-referenced against the location in which they live, which also allows for verification that no single worker was able to participate in the experiment more than once. Additionally, to ensure respondents are familiar with the instructions, we include multiple questions that explicitly instruct the participant to select a specific number as the answer. And, finally, to further insulate the experimental results from inattentive, hasty, or automated responses, we use hidden timers to track the time spent on each screen. After non-conforming responses were eliminated, a total of 995 responses were recorded. To encourage participants to fully engage in the experiment, we financially incentivize them by paying the top tercile of performers on a post experiment quiz a double bonus above and beyond their initial participation fee. The quiz asks a series of questions that subjects would only get correct if they took the time to read the information provided to them in the early screens of the experiment. While no process can ensure full cooperation, this form of incentivized compensation reflects the best practices used in experimental research. Note that to the extent subjects do not read and understand the experiment, results should tend towards a null finding. That is, significant results are less likely when subjects do not fully engage in the experiment. Consistent with Borghans et. al’s (2008) summary of how personality traits relate to behavioral economics, we collected data on the Big 5 personality traits: extraversion, agreeableness, conscientiousness, neuroticism, and openness. Financial literacy is measured by the accuracy of answers to five FINRA Investor Education Foundation questions. Financial literacy is suspected to be predictive of borrower behavior, as people who feel able to navigate riskier situations may feel they have more available choices (Dimmock, et. al, 2016; Zahirovic-Herbert, Gibler and Chatterjee, 2016; Lusardi and Mitchell, 2014; Guiso, Sapienza and Zingales, 2013). Finally, the experiment asked about participant’s home selling experience and traditional demographic information. The appendix shows the exact process through which participants advanced during the experiment as well as the information they were provided along the way to make informed decisions. Results One of the primary tests in Table 1 relates to seller anchoring around offer prices. When making a cash offer to a prospective home seller, iBuyers can either simply state the (bottom line) offer to the homeowner or start by sharing what they believe to be the fair market value and then subtract off fees and expenses before arriving at the final cash offer. From a purely financial perspective, theory would dictate that these two approaches will equally result in the homeowner accepting or rejecting the offer with the same probability because both offers are ultimately identical. The behavioral economic notion of anchoring, however, suggests these two different approaches might vary because the first number the homeowner sees is the one to which they will anchor, or be hesitant to deviate from. For example, if the iBuyer offers the bottom-line number of $500,000, the homeowner will determine a willingness to sell at $500,000. If instead, the iBuyer first explains they believe the home is worth $535,000, but after subtracting off fees and expenses, the iBuyer finally arrives at the same ultimate number of $500,000, the homeowner may not have the same willingness to sell because he has first anchored at $535,000 and is thus resistant to drop too far below that number[footnoteRef:2]. The presence of anchoring in iBuyer offers becomes an empirical question, which we answer in Table 1. [2: There is also a new line of thought on the idea of anchoring and the partitioning. While it is beyond the scope of this study, setting a sales price and then separating or partitioning the price to a lower net price may also play a role in our findings (Kim and Srivastava, 2022).] (Insert Table 1 here) Table 1 presents the results from 24 pools, testing not only anchoring, but also offer pricing strategy and various combinations of offers shared with the homeowner. Pools 1~8 reflect a “round” pricing strategy, where the cash offer is $500,000. Pools 9~16 reflects a “just above” pricing strategy in the amount of $501,200, whereas Pools 17~24 represent a “precise” pricing strategy in the exact amount of $501,236. Precise pricing strategies may signal to the seller both a greater certainty in the offer price and a lesser willingness to negotiate on the part of the buyer. It is worthy to note that the literature on pricing strategy is from the seller’s perspective, whereas the current investigation represents an offer price strategy on the part of the prospective buyer. In particular, Beracha and Seiler (2015) demonstrate sellers should list a property at a price just below a threshold so that the left-most digit will be lower than the one associated with the threshold (i.e., $199,000 as opposed to $200,000). Hence, a would-be buyer will perceive the home as costing less if the left-most digit is smaller. However, in the context of this study, the iBuyer is making a purchase offer, so they want the left-most digit to be as high as possible to affect perceptions and encourage the seller to sell at this higher number. Simply stated, if you want to transact an asset that is worth roughly $100, then offer to sell it at a list price of $99, but if you do not already possess it, then offer to buy it for $100. Since iBuyers are offering to buy the home, we use left-most digits that are at the next higher level (i.e., $500,000 as opposed to $499,000). Odd numbered pools (e.g., Pools 1, 3, 5, …, 23) reflect offers where the “fair market value” has been presented first followed by a listing of expenses and fees that lower the initial number to arrive at the ultimate selling price, whereas even numbered pools (i.e., Pools 2, 4, 6, …, 24) reflect treatments where only the bottom-line number is offered to homeowners. Differences in these results are deemed to reflect a pure test of anchoring because it is the only thing that differentiates between odd and even numbered pool pairs. The remaining sets of columns represent different choices home sellers have in the marketplace. For example, in addition to (1) selling the home to an iBuyer, the seller can also (2) list with the iBuyer’s brokerage arm without first making renovations, (3) list with the iBuyer’s brokerage arm after making home renovations, (4) list with a traditional broker without first making renovations, and (5) list with a traditional broker after making renovations. Empty columns reflect that option not being tested in that pool. The full description of each pool is presented in the footnote of Table 1, and the presentation of the exact screens seen by participants is shared in the appendix. Panel A presents the univariate results by pool, whereas Panel B aggregates results by compiling pools across pricing strategies. The main takeaway from Table 1 is that anchoring matters. Specifically, homeowners are twice as likely to accept a cash offer that presents to them a bottom line offer straight away as opposed to a top line fair market value followed by listing expenses and fees that eventually arrive at the same cash offer amount. In terms of offer price strategy, the results do not statistically differ. The final variable collected within the 24 pools is an Action Score, equal to 1 if the homeowner plans to take no action after being presented with the iBuyer offer to 7 if they definitely plan to take an action now. The reason for collecting this metric is because iBuyers state they do not prefer whether they obtain the home through a cash offer and then turn around and sell it in the market after a couple months or simply act as an intermediary broker and connect the seller directly to an ultimate buyer directly and receive a traditional commission. As can be seen in the right-most column of Table 1, with one exception, there is generally no difference in the homeowner’s likelihood of taking an action across pools. (Insert Table 2 here) Whereas Table 1 examined univariate results based only on variables controlled for directly in the pools themselves, Table 2 presents multivariate results where in addition to the treatments specifically laid out in the experimental design, we concatenate the data with a series of explanatory variables collected on the participants themselves. Alternatively explained, when randomly assigning participants to pools, it is believed that through the law of large numbers, any additional considerations/differences between participants will be evenly distributed across pools rendering the additional factors implicitly controlled for in the data. While this scientific approach is widely accepted in the social sciences, we take on the added burden of investigating several other factors that might impact our results. Specifically, we ask five additional questions to measure participant financial literacy and collect the Big Five personality traits. We further collect a series of behavioral characteristics such as level of confidence, optimism, whether the subject views their home as more of an investment versus a consumption good, how many homes they have sold in the past, and common demographic information including gender, education, income, age and so forth. Consistent with Table 1, Table 2 confirms through more rigorous logistic (Column 1) and OLS (Column 2) regressions that anchoring matters, whereas offer price strategy does not. Perceiving your home as more of an investment than a consumption good causes homeowners to want to list their property in the marketplace rather than selling to an iBuyer in the hopes of securing greater net proceeds, as expected. However, none of the other control variables made a significant difference in a homeowner’s preference between taking the cash offer as opposed to listing the property for sale in the traditional way. Given the nature of a randomized design, these null results are expected as it seems exogenous factors were evenly distributed across the pools. Concerning homeowners’ desire to act now versus waiting to make a selling decision, column (2) reports that more optimistic people and those who are more agreeable are more likely to act now as opposed to waiting. (Insert Table 3 here) iBuyers are typically repeat players in the residential real estate market, and as such, it is important for them to ensure sellers feel they made a good decision to sell to the iBuyer. Beyond indirect repetitional effects, these selling homeowners may want to also buy a home in the same market, or in a different market that the iBuyer also operates in, whether that be immediately or soon. In short, it is very much in the iBuyer’s best interest that sellers feel they did well in the home selling process. To this end, we estimate home seller regret when selling to an iBuyer. Our experiment measures regret aversion based on home price discounts between 1% and 12%, incremented at 1% intervals. Panel A reports in tabular form regret levels on a scale from 1 = regret to 7 = no regret when the iBuyer later sells the home at a higher price than they paid, whereas Panel B shares a graphical representation (see the appendix for exact wording). The column Regret 1 reflects the situation where no renovations have been performed, while Regret 2 shows results after the iBuyer added value through making cosmetic home repairs before reselling. Home sellers start to experience a non-trivial increase in regret around the 10% home price discount level, meaning that when they sell their home for a greater than 10% discount, they regret their decision to sell. However, this regret is significantly mitigated if they understand that their home underwent a renovation between sales. The question is then, how should iBuyers communicate to the seller that the reason their home resold for more than a 10% premium is because the iBuyer made renovations first? Should the iBuyer share the cost of those renovations and what was done to warrant the price difference? Would simply reaching out to the seller alert them to the discrepancy creating awareness to what might otherwise go unnoticed by the seller? On the other hand, if the iBuyer stays in contact with the seller, this salience might increase the probability to secure the seller as a client on future buying/selling services. Or lastly, should the buyer make offers to the seller that appear imprecise and rounded? Research shows that when a new price is revealed (in this case the next sale of the home which is assumed higher for this study), that the seller views the person who provided the imprecise value more trustworthy than a person who originally delivered a precise and subsequently revealed low price (Pena-Marin and Wu, 2019). This might also be reinformed as decision-makers tend to assign more weight to precise values making it more salient (Pena-Marin and Yan, 2021). All these considerations are beyond the scope of the current investigation but are worthy of future studies which we encourage others to undertake. The takeaway from this table is simply that discounts beyond 10% introduce levels of regret that should be a concern for repetitional effects, but that can be mitigated by communicating renovations have been made to explain the discount. (Insert Table 4 here) In the wake of the Financial Crisis in 2008, large institutional buyers stepped in and bought large swathes of distressed homes in depressed areas of the country at steep discounts. Some viewed this as positive because it propped up depressed home prices and helped unwind toxic assets on firms’ balance sheets, while others viewed it as just another corporate America land grab. iBuyers are aware of this public perception and want to know the extent to which prospective sellers are willing to sell to an institutional buyer. On the one hand, sellers may prefer to sell to a family because they have a vested interest in leaving the neighborhood in the best state possible. On the other hand, there are several reasons why the type of buyers should not matter. For example, the seller may be leaving the area and may only be focused on maximizing their personal wealth position, not that of their now former neighbors. Also, it might not matter that the immediate buyer is an iBuyer because the goal of the iBuyer is to sell to a family within a couple months anyway. At the same time, if the market turns from hot (the only market condition experienced by iBuyers) to cold, maybe iBuyers will resort to renting out the homes, just as buy-to-rent institutional sellers did after the Financial Crisis. In this case, iBuyers would be the owner of record, but presumably families would occupy the homes. But even this is uncertain because maybe the iBuyer would rent out the home on AirBnb or VRBO, which neighbors have been found not to prefer. With all these possibilities and disparate reasons to prefer selling to one party over another, it becomes an empirical question as to whether homeowners prefer to sell to iBuyers versus individual families directly. Table 4 reports the results from a test which measures preferences for selling to iBuyers as opposed to a family. A secondary investigation considers the offer price strategy, this time using slightly different numbers, but still examining round, just above and precise pricing strategies. A willingness to accept (WTA) score is reported ranging from 1 to 7 (see the appendix for exact wording). In all comparisons, neither buyer type (iBuyer vs. family) nor offer price strategy makes a statistically significant difference in WTA. (Insert Table 5 here) Homeowners generally understand that an opinion of value can be biased by the incentives of the party assessing the value of the home. Table 5 investigates homeowners’ perceived accuracy on a scale from 1 = not at all accurate to 7 = extremely accurate relating to various home value estimators (see the appendix for exact wording). Sorted in order from high to low, real estate listing agents are most trusted, followed by a lender’s appraisal, who have been found to appraise at or above the purchase price 90% of the time and exactly at the purchase price 30% of the time (Calem et. al, 2018). iBuyers rank in third place, which is not surprising given that they are directly making a purchase offer on the home, and presumably want to get as low a price as possible. The city tax assessor follows closely behind iBuyers in terms of perceived accuracy. What is interesting is that an individual investor, and much lower than that, a family looking to buy the home round out the least perceived home price accuracy of the groups tested. Since iBuyers, the individual investors, and families are all making offers on the home, it is interesting that iBuyers are by far perceived to be the most accurate assessors of true value amongst these three groups. (Insert Table 6 here) Since successful iBuying hinges on the accuracy of AVMs, we next investigate what causes a homeowner to place a high degree of confidence in a method of determining value. When building a hedonic model, the algorithm itself only gets you so far. Instead of focusing purely on a better algorithm, it makes sense to differentiate by generating better inputs. iBuyers recognize this and seek to improve the accuracy of their AVMs by encouraging homeowners to upload current pictures of their home into the iBuyer’s library of photos. This is important because when searching public records, it is often the case that photos of a home can be quite dated, possibly going back as far as the last time the home was listed for sale on a Multiple Listing Service (MLS). If a home has not been listed in many years, then the ability of an iBuyer, or any other AVM, to know the fair market value of a home can become quite limited. For this reason, Table 6 collects data from participants concerning what results in a more accurate AVM (see the appendix for exact wording). At the bottom of the list is a pure machine learning algorithm using only publicly available information. Ranking higher in perceived accuracy is a professional using publicly available information. So, the human wins out over the machine. Not surprisingly, when combining the machine with a professional, this combination results in an even higher score. But what tops the listed of perceived accuracy is a professional using current pictures of the home taken and uploaded by the homeowner[footnoteRef:3]. [3: As an interesting side note, iBuyers are considering branching off into other streams of revenue such as using the information collected from homeowner uploaded photos to sell marketing leads to local home improvement stores/providers who will pay iBuyers for more targeted lists to sell products and services to make home improvements. If this practice takes off, it might allow for iBuyers to further cut into their cash offer buying margins, which would presumably increase the demand for their service and improved efficiency in the housing market. But, we leave this for future studies to examine.] (Insert Table 7 here) Zillow is by far the most widely recognized name in reporting home values across the country. When asked how accurate people think Zestimate (Zillow’s AVM) is, generally speaking, people believe their AVM. This is certainly supported by Zillow’s claim to have extremely accurate AVMs despite the recent and very public observation that they grossly overpaid for many homes resulting in huge corporate losses. To more formally measure people’s perception of various firm AVM accuracy, we asked participants to share how accurate they think the AVMs are for several different companies (see the appendix for exact wording). These companies range from household names like Zillow, Equifax and FreddieMac to more B2B data providers like Corelogic. What we report in Table 7 is that homeowners generally associate perceived AVM accuracy with familiarity. For example, everyone knows who Zillow is, so they rate Zillow’s Zestimate as being most accurate, whereas most consumers are not familiar with the incredibly robust and heavily relied upon and respected Corelogic data, who ranked last. To further our claim that perceived AVM accuracy is simply a proxy for a popularity contest, we introduced two fictitious companies, TruPrice and Precision, whose names strongly convey accuracy, but because they do not actually exist, have no name recognition at all. As proper controls should behave, these two companies were rated as the lowest and second lowest organizations on the list. The conclusion to be reached is that if you want homeowners to believe you know what you’re doing simply invest more in marketing to make your company’s name salient. Conclusions The use of PropTech to allow for all-cash buying is a burgeoning activity across major housing markets throughout the country, yet very few studies have investigated the behavioral considerations for these firms. This study seeks to fill the gap in the nascent literature by presenting a series of experiments relating to several important iBuyer actions and what this means for prospective home sellers. We find that when presenting a cash offer to sellers, iBuyers should directly present the bottom line offer as opposed to starting with a top line fair market value and then subtract fees and expenses before finally arriving at the cash offer price. This holds true in both a univariate and multivariate context. When deciding between accepting the cash offer from an iBuyer or listing the property for sale in a traditional brokerage setting, sellers who view their home as more of an investment (as opposed to a consumption good) are more likely to list in the hope of obtaining higher net proceeds. The offer price strategy used by the iBuyer (e.g., round, just above, and precise) does not matter in any of the contexts we investigated, and sellers do not prefer selling to a family versus an iBuyer. However, the PropTech firms may prevent seller’s regret of their customers by offering a discount of less than 10% or explaining that home improvements made before the subsequent resale justified the price hike. Only then are iBuyers likely to retain the reputational capital important in a repeat game business. Homeowners tend to still perceive traditional sales agents as being more accurate in their opinion of value, but when that agent augments their value estimate with actual photos of the home taken and uploaded directly by the homeowner, their perceived accuracy increases even further. Finally, firms who want homeowners to place a high degree of confidence in their AVM estimate need not worry as much about accuracy as they do about general market awareness of their company’s existence as marketing plays a huge role in perceptions. The apparent success of form over substance makes it likely that AVMs will continue to be less accurate than they need to be to help ensure iBuyer profitability. Future work may focus on the role of iBuyers in one of the largest value markets in existence and certainly one of the most relevant to the individual in terms of wealth, diversification, and importance. References Arrow, Kenneth J. 1974. Limited Knowledge and Economic Analysis, American Economic Review, 64(1), xiii-10. Beracha, Eli and Michael J. Seiler. 2014. The Effect of Listing Price Strategy on Transaction Selling Prices, Journal of Real Estate Finance and Economics, 49(2), 237-255. Beracha, Eli, and Michael J. Seiler. 2015. The Effect of Pricing Strategy on Home Selection and Transaction Prices: An Investigation of the Left-Most Digit Effect, Journal of Housing Research, 24(2), 147-161. Bernheim, B. Douglas, and Jonathan Meer. 2008. Do Real Estate Brokers Add Value When Listing Services are Unbundled? 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The Economic Importance of Financial Literacy: Theory and Evidence, Journal of Economic Literature, 52(1), 5–44. Marshall, Alfred. 1975. Early Economic Writings of Alfred Marshall 1867-1890, J. Whitaker (ed.), London, Macmillan. Oishi, Shigehiro, and Shelly Tsang. 2022. The Socio‐Ecological Psychology of Residential Mobility, Journal of Consumer Psychology, May, 1-18. Ottati, GabiDei. 1994. Trust, Interlinking Transactions and Credit in the Industrial District, Cambridge Journal of Economics, 18(6), 529-546. Pena‐Marin, Jorge, Dengfeng Yan. 2021. Reliance on Numerical Precision: Compatibility Between Accuracy Versus Efficiency Goals and Numerical Precision Level Influence Attribute Weighting in Two‐Stage Decisions, Journal of Consumer Psychology, 31(1), 22-36. Pena‐Marin, Jorge, and Ruomeng Wu. 2019. Disconfirming Expectations: Incorrect Imprecise (vs. Precise) Estimates Increase Source Trustworthiness and Consumer Loyalty, Journal of Consumer Psychology, 29(4), 623-641. Seiler, Michael J., Vicky L. Seiler, and Mark A. Lane. 2012. Mental Accounting and False Reference Points in Real Estate Investment Decision Making, Journal of Behavioral Finance, 13(1), 17-26. Seiler, Michael J., Vicky L. Seiler, Stefan Traub, and David M. Harrison. 2008. Regret Aversion and False Reference Points in Residential Real Estate, Journal of Real Estate Research, 30(4), 461-474. Seiler, Michael J., and Liuming Yang. 2022. The Burgeoning Role of iBuyers in the Housing Market, Real Estate Economics, forthcoming. Simpson, Brent, and Tucker McGrimmon. 2008. Trust and Embedded Markets: A Multi-Method Investigation of Consumer Transactions. Social Networks, 30(1), 1-15. Sun, Hua, and Michael J. Seiler. 2013. Hyperbolic Discounting, Reference Dependence and its Implications for the Housing Market, Journal of Real Estate Research, 35(1), 1-23. Swope, Kurtis J., John Cadigan, and Pamela Schmitt. 2014. That’s My Final Offer! Bargaining Behavior with Costly Delay and Credible Commitment, Journal of Behavioral and Experimental Economics, 49 (April), 44-53. Venkateswaran, Viswanathan, Deepak S. Kumar, and Deepak Gupta. 2021. To Trust or Not: Impact of Camouflage Strategies on Trust in the Sharing Economy, Journal of Business Research, 136(C), 110-126. Yoon, Sangsuk and Nathan Fong. 2019. Uninformative Anchors Have Persistent Effects on Valuation Judgments Journal of Consumer Psychology. 29(3), 391-410. Zahirovic-Herbert, Velma, Karen M. Gibler, and Swarn Chatterjee. 2016. Financial Literacy, Risky Mortgages, and Delinquency in the US during the Financial Crisis, International Journal of Housing Markets and Analysis, 9(2), 164–189. Zillante, Artie, Dustin C. Read, and Michael J. Seiler. 2019. Using Prospect Theory to Better Understand the Impact of Uncertainty on Real Estate Negotiations, Journal of Real Estate Research, 41(1), 75-105. 1 1 Table 1. Pool Descriptive Statistics This table reports the descriptions of the 24 pools. Cash Offer prices are reflected in Pools 1-8 ($500,000); Pools 9-16 ($501,200); Pools 17-24 ($501,236); Action Score is a 7-point scale where 1 = Wait to make a decision, through 7 = Take an action now. *** indicates significance at the 0.01 level; ** indicates significance at the 0.05 level; * indicates significance at the 0.10 level. Cash Offer Listing with iBuyer Listing with Traditional Broker Sample Size Action Score (1) (2) (3) (4) (5) 995 4.99 iBuyer withOUT Renovations iBuyer WITH Renovations Traditional Broker withOUT Renovations Traditional Broker WITH Renovations Panel A: Pools Presented Individually Pool 1 - Fees 13.9% 13.9% 63.9% 0.0% 8.3% 36 4.86 Pool 2 - No Fees 14.9% 14.9% 63.8% 2.1% 4.3% 47 4.79 Pool 3 - Fees 8.7% 82.6% 8.7% 46 4.93 Pool 4 - No Fees 20.5% 76.9% 2.6% 39 4.72 Pool 5 - Fees 17.8% 33.3% 48.9% 45 4.53 Pool 6 - No Fees 26.2% 23.8% 50.0% 42 4.79 Pool 7 - Fees 21.1% 78.9% 38 5.34 Pool 8 - No Fees 48.9% 51.1% 47 5.15 Pool 9 - Fees 8.1% 21.6% 54.1% 2.7% 13.5% 37 4.84 Pool 10 - No Fees 33.3% 12.5% 45.8% 4.2% 4.2% 48 5.38 Pool 11 - Fees 11.4% 81.8% 6.8% 44 5.09 Pool 12 - No Fees 22.7% 72.7% 4.5% 44 5.55 Pool 13 - Fees 10.9% 32.6% 56.5% 46 5.15 Pool 14 - No Fees 29.5% 29.5% 40.9% 44 4.86 Pool 15 - Fees 19.4% 80.6% 36 4.97 Pool 16 - No Fees 52.5% 47.5% 40 5.18 Pool 17 - Fees 8.6% 14.3% 60.0% 5.7% 11.4% 35 5.14* Pool 18 - No Fees 23.3% 11.6% 55.8% 0.0% 9.3% 43 4.44* Pool 19 - Fees 23.5% 67.6% 8.8% 34 4.76 Pool 20 - No Fees 31.1% 57.8% 11.1% 45 4.93 Pool 21 - Fees 22.2% 19.4% 58.3% 36 4.97 Pool 22 - No Fees 31.7% 22.0% 46.3% 41 4.78 Pool 23 - Fees 36.4% 63.6% 33 5.33 Pool 24 - No Fees 36.7% 63.3% 49 5.22 Panel B: Combining Pools across Offer Price Strategies Pools 1,9,17 10.1% 16.5% 58.7% 2.8% 11.9% 108 4.88 Pools 2,10,18 23.9% 13.0% 55.1% 2.2% 5.8% 138 4.94 Pools 3,11,19 13.7% 78.2% 8.1% 124 4.94 Pools 4,12,20 25.0% 68.8% 6.3% 128 5.08 Pools 5,13,21 16.5% 29.1% 54.3% 127 4.88 Pools 6,14,22 29.1% 25.2% 45.7% 127 4.81 Pools 7,15,23 25.2% 74.8% 107 5.21 Pools 8,16,24 45.6% 54.4% 136 5.18 NOTE: Pool 1 ($500,000; w/ cash fees; w/ renovation; w/ competitor); Pool 2 ($500,000; w/o cash fees; w/ renovation; w/ competitor); Pool 3 ($500,000; w/ cash fees; w/o renovation; w/ competitor); Pool 4 ($500,000; w/o cash fees; w/o renovation; w/ competitor); Pool 5 ($500,000; w/ cash fees; w/ renovation; w/o competitor); Pool 6 ($500,000; w/o cash fees; w/ renovation; w/o competitor); Pool 7 ($500,000; w/ cash fees; w/o renovation; w/o competitor); Pool 8 ($500,000; w/o cash fees; w/o renovation; w/o competitor); Pool 9 ($501,200; w/ cash fees; w/ renovation; w/ competitor); Pool 10 ($501,200; w/o cash fees; w/ renovation; w/ competitor); Pool 11 ($501,200; w/ cash fees; w/o renovation; w/ competitor); Pool 12 ($501,200; w/o cash fees; w/o renovation; w/ competitor); Pool 13 ($501,200; w/ cash fees; w/ renovation; w/o competitor); Pool 14 ($501,200; w/o cash fees; w/ renovation; w/o competitor); Pool 15 ($501,200; w/ cash fees; w/o renovation; w/o competitor); Pool 16 ($501,200; w/o cash fees; w/o renovation; w/o competitor); Pool 17 ($501,236; w/ cash fees; w/ renovation; w/ competitor); Pool 18 ($501,236; w/o cash fees; w/ renovation; w/ competitor); Pool 19 ($501,236; w/ cash fees; w/o renovation; w/ competitor); Pool 20 ($501,236; w/o cash fees; w/o renovation; w/ competitor); Pool 21 ($501,236; w/ cash fees; w/ renovation; w/o competitor); Pool 22 ($501,236; w/o cash fees; w/ renovation; w/o competitor); Pool 23 ($501,236; w/ cash fees; w/o renovation; w/o competitor); Pool 24 ($501,236; w/o cash fees; w/o renovation; w/o competitor). Table 2. Logistic & OLS Regression Results Explaining the (1) Cash Offer vs. List Decision and (2) Action Taken vs. Inaction by the Homeowner This table reports in column (1) the results of a logistic regression where the dependent variable = 0 if the homeowner chose the cash offer; 1 if they decided to list; Column (2) shows OLS regression results where the dependent variable =1 if the homeowner should wait to make a decision through 7 if they should take an action (cash or list) now; Fee Pools Dummy = 1 for pools listing fees associated with the cash offer, 0 otherwise; Round Price Dummy =1 for pools where the cash offer is $500,000, 0 otherwise; Exact Price Dummy =1 for pools where the cash offer price is $501,236, 0 otherwise; Pools FE? conveys whether Fixed Effects are included for the various pools; Financial Literacy = number of correct responses to the 5 financial literacy questions from FINRA; Number of Homes Sold = is over their lifetime; Investment vs. Consumption = 1 if home viewed as more of an investment through 7 = more as a consumption good; Confidence = 1if they self-rate as a below average driver through 7 = above average; Optimism = 1 if they do not expect good things to happen to them through 7 if they do. Big Five Personality Traits include Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness, as measured by Rammstedt and John (2007); Child Dummy = 1 if the homeowner has at least one dependent child living at home, 0 otherwise; Married Dummy = 1 if married, 0 otherwise; Male Dummy = 1 for males, 0 otherwise; Age of borrower, in years; Income on a scale from 1 (under $20,000) to 7 (over $120,000); Caucasian Dummy = 1 if Caucasian, 0 otherwise. College Dummy = 1 if at least has a 4-year college degree, 0 otherwise; Positive Net Worth = 1 if net worth is greater than 0, 0 otherwise. * indicates statistical significance at the 10% level; ** indicates statistical significance at the 5% level; *** indicates statistical significance at the 1% level. Cash vs. List Action vs. Inaction Intercept 2.251 (1.868) 3.995*** (0.803) Experimental Design Pools Fee Pools Dummy 0.860*** (0.305) Round Price Dummy 0.065 (0.355) Exact Price Dummy 0.128 (0.360) Pools FE? NO YES Behavioral Characteristics Financial Literacy 0.130 (0.150) -0.033 (0.059) Number of Homes Sold 0.168 (0.126) 0.046 (0.043) Investment vs. Consumption -0.221** (0.103) 0.078* (0.041) Confidence -0.048 (0.138) -0.014 (0.053) Optimism -0.125 0.142 (0.107) (0.045)*** Personality Characteristics Extraversion 0.006 (0.140) 0.031 (0.056) Agreeableness 0.003 (0.200) -0.197** (0.080) Conscientiousness -0.097 (0.201) 0.103 (0.001) Neuroticism -0.093 (0.165) 0.015 (0.065) Openness 0.051 (0.147) -0.001 (0.060) Demographics Child Dummy -0.332 (0.358) 0.046 (0.136) Male Dummy -0.109 (0.333) -0.109 (0.128) Married Dummy 0.155 (0.196) 0.151 (0.139) Age 0.001 (0.014) -0.001 (0.005) Income -0.046 (0.087) 0.017 (0.038) Caucasian Dummy 0.105 (0.350) 0.117 (0.145) College Dummy -0.067 -0.013 (341) (0.136) Positive Net Worth 0.268 -0.219 (0.356) (0.144) Observations 243 995 Chi-Square – F 25.78 1.318 p-value 0.215 0.090* -2 Log Likelihood 293.49 Cox & Snell R-Square 0.101 Nagelkerke R-Square 0.138 Correct Classification Percentage 63.4% Table 3. Seller Regret with and without Renovation Panel A of this table reports the average level of anticipated regret when a seller accepts a cash offer and later learns the iBuyer sold the home months later for more money. Panel B provides the graph. Integer values tested range from 1% through 12% (x-axis). p-values test for significant differences between each kink in the lines. Level of regret ranges from 1 through 7 (y-axis: where 1 = Regret; 7 = No Regret). Regret 1 (No Renovations) has a significant slope (β = -0.128, t-stat = -5.087; p = 0.00) as does Regret 2 (Renovations) (β = -0.131, t-stat = -5.306; p = 0.00). *** indicates significance at the 0.01 level; ** indicates significance at the 0.05 level; * indicates significance at the 0.10 level. Panel A: Table of the Results Discount % N Regret 1 N Regret 2 T-stat p-value 1% 42 5.38 43 6.00 1.749 0.084* 2% 47 5.19 39 5.59 1.208 0.231 3% 45 4.64 42 5.17 1.278 0.205 4% 40 4.58 37 5.19 1.361 0.177 5% 45 4.67 46 5.43 1.991 0.050** 6% 41 4.76 38 5.05 0.686 0.495 7% 38 4.26 44 5.45 3.144 0.002*** 8% 43 4.51 39 5.00 1.046 0.299 9% 41 3.90 44 4.59 1.518 0.133 10% 42 4.50 42 5.07 1.284 0.203 11% 33 3.55 40 4.23 1.389 0.169 12% 46 3.80 38 4.13 0.681 0.498 Panel B: Graphical Representation of Seller Regret Table 4: Homebuyer Type and Offer Price Strategy This tables examines two types of buyers: a family versus an iBuyer, to measure if sellers are differentially willing to accept an offer depending on the source of that offer. Offer price strategy is also considered (i.e., $600,000; $602,700; $601,352) to learn if round versus precise offers affect willingness to accept (WTA). Neither variable has a significant impact. Buyer Price Score N T-stat/ F-stat p-value (1) Family $600,000 5.30 166 (2) Family $602,700 4.99 171 (3) Family $601,352 5.45 165 (4) iBuyer $600,000 5.28 179 (5) iBuyer $602,700 5.20 157 (6) iBuyer $601,352 5.22 157 (1) v. (4) 345 0.062 0.950 (2) v. (5) 328 1.199 0.232 (3) v. (6) 322 1.429 0.154 (1-3) v. (4-6) 995 0.101 0.920 (1) thru (6) 995 1.655 0.143 Table 5. Source Accuracy in Home Value Estimates This table reports how homeowners perceive various independent sources who convey their opinion of home value. N represents sample size in each category. 1 = Not at all accurate through 7 = extremely accurate. The percentages within the table represent the percentage of homeowners who indicate a specific score. Source N 1 2 3 4 5 6 7 Mean Score Real Estate Agent working to sell your home 995 0.5% 2.0% 4.4% 25.2% 26.5% 30.7% 10.7% 5.10 Lender’s Appraiser 995 0.7% 2.3% 10.6% 27.5% 24.3% 24.4% 10.2% 4.86 An iBuyer looking to buy your home 995 2.6% 5.2% 14.5% 35.4% 24.5% 12.8% 5.0% 4.32 City Tax Assessor 995 5.8% 9.2% 16.3% 26.3% 17.8% 14.6% 9.9% 4.25 An individual investor looking to buy your home 995 8.0% 16.7% 24.5% 25.5% 13.8% 8.8% 2.6% 3.57 A family looking to buy your home 995 12.8% 22.6% 30.4% 20.6% 8.1% 4.3% 1.2% 3.07   Table 6. Method Accuracy in Home Value Estimates This table reports how homeowners perceive various methods to determine home value. N represents sample size in each category. 1 = Not at all accurate through 7 = extremely accurate. The percentages within the table represent the percentage of homeowners who indicate a specific score. Variable N 1 2 3 4 5 6 7 Mean Score A professional using public information and photos the homeowner took and shared 995 1.1% 3.6% 7.9% 28.1% 29.8% 22.4% 6.9% 4.77 A machine learning algorithm and a professional using public information 995 2.0% 4.3% 9.5% 25.0% 28.5% 21.1% 9.4% 4.75 A professional using public information 995 1.4% 3.5% 10.6% 37.6% 25.7% 17.0% 4.2% 4.51 A machine learning algorithm using public information 995 3.1% 8.1% 17.6% 32.5% 24.8% 11.2% 2.7% 4.12   Table 7. Organizational Accuracy in Home Value Estimates This table reports how homeowners perceive the accuracy of various organization’s estimates of home value. N represents sample size in each category. 1 = Not at all accurate through 7 = extremely accurate. The percentages within the table represent the percentage of homeowners who indicate a specific score. NOTE: Sample sizes can be smaller than the full sample size of 995 because we allow the homeowner to indicate they do not know the organization. In these cases, the observation is omitted. In this sense, N represents familiarity with the organization. Precision and TruPrice do not exist. These fictitious firms were introduced as controls to get a baseline for how many homeowners will report knowing of an organization’s AVM when in fact they cannot. Organization Name N 1 2 3 4 5 6 7 Mean Score Zillow 957 1.8% 2.8% 9.4% 28.0% 21.2% 26.6% 10.1% 4.85 Equifax 800 2.5% 4.5% 8.8% 30.3% 21.4% 18.9% 13.8% 4.75 FreddieMac 757 2.1% 3.3% 9.2% 34.3% 20.9% 20.1% 10.0% 4.69 Redfin 750 1.9% 2.8% 10.7% 38.3% 22.8% 15.1% 8.5% 4.57 TruPrice 558 2.5% 5.0% 9.5% 43.7% 19.7% 14.0% 5.6% 4.37 Precision 503 2.0% 4.2% 10.7% 51.9% 15.1% 12.1% 4.0% 4.26 Corelogic 504 2.4% 3.8% 12.3% 47.8% 19.0% 10.7% 4.0% 4.25   Appendix: Outline of the Experimental Design Consent Page (suppressed to conceal author identity) (Next Page) We’re conducting a study on iBuying – firms who offer to either (1) buy a home in cash now or (2) list it for sale in the way firms have done for years. We will provide you with all you need to know to complete this HIT.  And to encourage you to read the details, we will pay you DOUBLE for this task if you are among the top 1/3 performers who demonstrate understanding. Please READ all screens very CAREFULLY!! (Next Page) Benefits to accepting a CASH Offer: Certainty: - A guaranteed, lower amount now vs. an uncertain, higher amount later. - Traditional sales can fall through (appraisal, financing, fickle buyers). Financial: - Avoid paying real estate agent fees (typically 6%). - Get cash fast to make an investment, settle a divorce/family estate, or consolidate debt. Convenience: - No prep work, showings or Open Houses - You pick the moving date and deposit the check --------------------------------------------------------------------------------------------------------------- [Note to Reader]: There are 24 pools (based on the treatments listed below) that compose the first test. Introductory screens give participants just enough context to complete the experiment, but without revealing our research questions. Subjects see only 1 of the 24 pools and have no idea that the other pools even exist. Depending on the pool, more or fewer explanatory pages were shared with the participant in order for them to be able to informatively answer the question. Experimental Design for the first test: 2x2x2x3 design = 24 Pools (2) Fees vs. No fees (2) Renovations vs. No Renovations (2) Listing with iBuyer vs. Traditional Broker (3) Offer Price Strategies: Round ($500,000) vs. Precise Price 1 ($501,200) vs. Precise Price 2 ($501,236) The next pages ONLY apply to pools where the subject needs the information. In the simplest pool where only price is presented, there is no discussion of renovations or listing with another firm. ---------------------------------------------------------------------------------------------------------------- (Next Page – ONLY in select pools) The Choice to Renovate: If you decide to list your home for sale: You can list the home in its current condition, or the iBuyer will suggest minor renovations before listing. You get to choose which renovations to make, if any. This will delay the sale of your home, but ideally, the renovations will yield a higher selling price. In both cases, the iBuyer will provide you with a local agent, market your home on popular websites (like Zillow), host open houses, and negotiate the sale for you. The iBuyer will pay for the renovations you choose to make and collect that money back when the home sells. If for any reason the home doesn't sell, you have to reimburse them the costs of the renovations. (Next Page – ONLY in select pools) Choosing a Broker: If you decide to list your home for sale, you can List through the iBuyer, or List through a traditional brokerage firm. The iBuyer charges a lower selling fee and you can further benefit if you also use the iBuyer to buy your next home within 6 months. Because of the lower selling fee, other brokers describe this iBuyer as a “discount broker” (implying a lower level of service) and claim the iBuyer’s agents are not local and therefore do not know the local market. The iBuyer refutes these claims. (New Page – Example of the most complex pool) A good friend of yours wants to sell his home. He has 5 ways to sell – summarized by the chart below. Please select the option you recommend to your good friend by clicking inside the column. You may only select one. Cash Offer Listing with This Company Listing with Other Company This Company - Low This Company - High Other Company - Low Other Company - High Starting Point $538,000 $538,000 $565,000 $538,000 $565,000 - Selling Fees - 6% - 5% - 5% - 5.5% - 5.5% - Closing Costs -1% -1% -1% -1% -1% - Repair Costs 0% 0% -3% 0% -3% (Estimated) Net Proceeds $500,000 $505,720 $514,150 $503,030 $511,325 Please rate how likely you are to (1) Wait to make a decision OR (7) Take an action now (Cash offer now or List the property for sale). Wait to make a decision Undecided Take an action now (Cash or List) 1 2 3 4 5 6 7 If you were to take an action now, please indicate how likely you are to take the (1) Cash offer now versus (7) Listing the property for sale. Take the Cash Offer Undecided List the Property 1 2 3 4 5 6 7 (Next Page – ONLY in select pools) If you were to List now, please indicate how likely you are to (1) List with this company versus (7) List with a different brokerage. List with this company Undecided List with a different brokerage 1 2 3 4 5 6 7 [Note to Reader: All 24 independent participant paths now randomly split into 2 different paths [renovation vs. no renovation] to answer just one of the following questions. Again, each participant only sees one of these two questions and does not know the other exists. Note that both paths have random integer values that range from 1% to 12%.] [Note to Reader: No Renovations Treatment] If you took the cash offer and in a couple months saw the firm who bought your home sold it for <1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%> more, how would you feel? Wish I would have listed the property for sale instead of taking the cash offer Fine having accepted the lower cash offer instead of listing the property for sale 1 2 3 4 5 6 7 [Note to Reader: Renovations Treatment] If you took the iBuyer's cash offer, then saw they updated parts of your home and resold it a couple months later for <1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%> more than they paid you for it, how would you feel? Wish I would have listed the property for sale instead of taking the cash offer Fine having accepted the lower cash offer instead of listing the property for sale 1 2 3 4 5 6 7 [Note to Reader: Here participants are split into 6 (2x3) total paths reflecting selling to (a family versus an iBuyer) and across three different pricing strategies (i.e., round, just above and precise)]. Your cousin received an offer from to buy their house in cash right now for <$600,000; $602,700; $601,352>. How would you advise your good friend? Do NOT accept the Cash Offer Undecided Accept the Cash Offer 1 2 3 4 5 6 7 [Note to Reader: All participant paths now rejoin so everyone answers the remaining questions] [Note to Reader: This is the test subjects took to measure how much effort they took into understanding the experiment.] Please indicate if the following statements are TRUE or FALSE. True False I will likely receive a higher price if I take the cash offer now. X I can avoid paying real estate agent fees if I take the cash offer now X A cash offer now doesn’t allowed me to pick my moving date X Sales when listing a property can fall through due to a problem with financing X A cash offer avoids the need for Open Houses and property showings X Listing the property is a better option if I need cash to settle a divorce or consolidate debt? X A guaranteed lower amount now is worth an uncertain, higher amount later X I might make more if I sell with an agent on the open market, but there’s more risk and hassles. X Listing the property is a better option if I took a job out of town and need to relocate in 2 weeks. X Sellers don’t always achieve their listing price X The following companies have their own computer algorithm to determine the value of your home. How accurate do you believe they are? I have no idea 1= Not at all Accurate 2 3 4 = Average Accuracy 5 6 7 = Extremely Accurate Zillow Precision CoreLogic Redfin TruPrice Freddie Mac Equifax Several approaches are used to determine the value of your home. How accurate do you believe they are? 1= Not at all Accurate 2 3 4 = Average Accuracy 5 6 7 = Extremely Accurate A person using public information found in a database A combination of a person and machine learning algorithm using public information found in a database A person using public information found in a database and photos the homeowner took and shared A machine learning algorithm using public information found in a database An independent source informs you of the value of your home. Please rate how accurate you think these values are. 1= Not at all Accurate 2 3 4 = Average Accuracy 5 6 7 = Extremely Accurate Real Estate Agent wanting to sell your home A family looking to buy your home City Tax Assessor A firm looking to buy your home Lender’s Appraiser An individual investor looking to buy your home How many homes have you sold in your lifetime? Drop down menu Overall, I expect more good things to happen to me than bad. Disagree Agree 1 2 3 4 5 6 7 Compared to the average driver, rate your ability to drive a car. Far Below Average Average Far Above Average 1 2 3 4 5 6 7 Do you view your primary residence (where you live) as more of a “home” or more of an “investment”? More of an “Investment” Both a “home” and an “investment” equally More as a “Home” 1 2 3 4 5 6 7 [Note to Reader: We finish by including the 5 Financial Literacy questions, iBuyer test questions, other controls variables specific to this decision, the regular demographic variables, and the 5 personality traits (11 questions)]. Seller Regret (1 = Regret; 7 = No Regret) No Renovation 5.38 5.19 4.6399999999999997 4.58 4.67 4.76 4.26 4.51 3.9 4.5 3.55 3.8 Renovation 6 5.59 5.17 5.19 5.43 5.05 5.45 5 4.59 5.07 4.2300000000000004 4.13