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We challenge the conventional approach to distinguish between professional and non-professional Airbnb hosts by solely using the number of managed listings.

We leverage the recently released platform policy that categorizes hosts' professionalism by their self-declared status. Our multinomial modeling approach predicts true host status, factoring in the number of managed listings and controlling for listing and host traits. We employ data from five major European cities collected through scraping the Airbnb webpage.

Our research reveals that relying solely on the number of listings managed falls short of accurately predicting the host type, leading to difficulties in evaluating the platform's impact on the local housing market and reducing the effectiveness of policy intervention. Moreover, we advocate using more fine-grained measures to differentiate further between semi-professional and professional hosts who exhibit heterogeneous economic behaviors.

Reliable professionalism metrics are essential to curb unethical practices, promote market transparency and ensure a level playing field for all market participants.

This work pioneers the revelation of the inadequacy of a commonly used measure for predicting host professionalism accurately.

The aim of this paper is to focus on different compensation structures for real estate mutual fund Management Companies and assess whether management fees paid on either Net Asset Value (NAV) or Gross Asset Value (GAV) generate distorted incentives relative to those generated by performance fees paid on the market value of the fund.

To test whether management fees induce Management Companies to opportunistic behaviors, the relative effect of NAV- and GAV-based fees is compared over time using a plethora of econometric models.

It is found that Management Companies that are paid GAV-based fees start with higher leverage to expand assets under management, then, subsequently, drive leverage and over-investment down as fund maturity approaches to minimize the negative impact of negative NPV investments on the final market value of the fund and therefore on performance fees paid at maturity.

A dataset of Italian listed real estate mutual funds is used. While the Italian market can be considered an ideal setting for an empirical analysis, studies on other countries would make it possible to test implications of the model that are only weakly identified in our setting.

Results could be important when designing managerial contracts.

It is shown that Management Companies actively manage the size of their balance sheet to maximize fees, and that NAV-based fees produce effects similar to market-based fees in terms of managerial incentives.