Bed and breakfast

For other uses, see Bed & Breakfast (disambiguation).

Breakfast at a B&B in Quebec City, Canada

The ship Bürgermeister O´Swald II is now a B&B in the port of Cuxhaven.

A bed and breakfast (typically shortened to B&B or BnB) is a small lodging establishment that offers overnight accommodation and breakfast. In addition, a B&B sometimes has the hosts living in the house.

Bed and breakfast is also used to describe the level of catering included in a hotel's room prices, as opposed to room-only, half-board, or full-board.

Introduction Purpose of the Analysis This paper aims at identifying some of the factors that lead to higher Airbnb listing prices. The short-term rental market is oversaturated now, so renters and landlords need to comprehend what causes the price fluctuations. To renters they ascertain those aspects that will enable cost-conscious selections even when optimizing for quality and comfort. Some of the information is beneficial to landlords on ways of increasing prices, upgrading features, and crafting better marketing strategies that will help them attract more guests while generating higher revenues. This paper seeks to close this research gap by establishing key variables that affect listing prices.

Data Source and Structure The dataset used for the current work has been derived from Airbnb website and covers all the provided listings along with the detailed characteristics of each property, as well as the performance indicators. It consists of 75 co-variants each giving specific information pertaining to the listing. Some of the relevant criteria are property attributes such as, room type, property type, number of bedrooms, host's attributes, availability and rating. Also included are listing price data as the dependent variable for this analysis, financial information is also included in the dataset. Therefore in order to make the analysis meaningful, tracking of seven independent variables has been done based on the pricing aspect. These include: Room Type: Options as whole home/apartment, private room, or shared room. Property Type: The category of the property including house, apartment or guest suite. Number of Bedrooms: A measure that suggests the size of the listing Review Scores Rating: The average score given by people who stayed in that particular property. Number of Reviews: Because the listing is widely used and relied on it should work seamlessly toward giving it a better and updated format. Accommodates: The number of guests the specific listing can accommodate to the maximum. Availability (30 days): The short-term availability of the property which refers to the amount of time within which a property owner may offer their property, for rent or sale to the targeted clients. These variables where chosen since there are many potential varying factors that can affect the price within reach. In order to process the missing values and the peculiarity of the Values column, the data was cleaned for categorical inputs such as Room Type and Property type. The inputs were encoded to one-hot as required for the regression model.

Significance of the Analysis The findings presented in this analysis are useful for both the renting parties and the owning parties. The factors highlighted here will help in understanding the determinants of listing prices when considering various options. Budget-conscious consumers often seek options based on specific criteria. For instance, higher ratings in reviews or a larger number of guests accommodated can influence pricing. For landlords, the concluding part of the analysis provides strategic recommendations to enhance the performance of their listings. By implementing these strategies, landlords can improve their revenue and attract more tenants. This analysis advances knowledge about the nature and patterns of short-term rentals. It identifies various factors that guests consider important, such as higher ratings and the number of guests accommodated. This information helps both renters and landlords make informed decisions.

Summary of Approach The parameter was selected through multiple regression analysis to evaluate the impact of various factors on listing prices. This methodology applies to all involved parties in the Airbnb market.

Data Overview of the Dataset The study used a comprehensive dataset to identify variables likely to offer clues on the features that affect listing prices. Important variables obtained from the dataset include property features such as room type, amenities, and location. The variables selected for analysis were crucial for understanding the factors influencing listing prices.

Variables Selected for Analysis

Room Type: This variable describes the type of room offered, such as an entire apartment, private room, or shared room. It affects the level of privacy and the presence of certain facilities and services, which in turn determines the cost. Property Type: This variable characterizes the place of lodging, whether it is a house, apartment, or other types of properties. It can also determine the value, ranging from luxurious to more practical properties. Number of Bedrooms: This represents the size of the house and its ability to accommodate a group of people, which explains why house prices are high. Review Scores Rating: This is the average rating given by guests, reflecting the general impression of the property and services. It is hypothesized that as the scores increase, there should be a direct correlation with the price. Number of Reviews: This refers to the number of reviews left on the listing and the extent of confidence people place in the listing. Accommodates: This factor is related to the capacity of the property to accommodate guests, influencing the price of the listing. Availability (100-days): Availability in the short term can indicate the popularity of the listing, as more availability may suggest that the listing is ready to host.

Creation of New Variables Among the variables used in the model, one was derived specifically for this paper: the Luxury Score. This score was developed to sum up a property’s desirability. The number of bedrooms usually correlates with a higher luxury score, indicating a more luxurious listing. High maintenance and availability also contribute to the luxury score, setting up a benchmark for luxury properties.

Data Cleaning Criteria Before running the analysis, the dataset underwent rigorous cleaning to ensure accuracy and reliability. Handling Missing Values: For objective analysis, any rows that contained missing values in key variables such as price, room type, or review scores were dropped. Data Normalization: On some occasions, categorical variables such as price were converted to a common format (e.g., replacing symbols like dollars with a common symbol). One-Hot Encoding: Categorical variables like room type and property type were one-hot encoded so they could be used in the regression model. Outlier Removal: Some of the extreme observations were filtered out to get a better view of the remaining data, such as cases where the prices were exceptionally high or low. By performing these steps, the dataset was cleaned and formatted to allow for a thorough analysis of the factors driving listing prices.

Regression Model Overview For the purpose of estimating and articulating prices, an Ordinary Least Squares (OLS) regression model was used. This approach determines the magnitude and significance of the connection between the dependent variable (listing prices) and the independent variables. Therefore, the role of the independent variables is to measure the correlation with the dependent variable, providing a framework for analyzing market trends. This model helps in understanding the impact of various factors on prices and assessing the model’s performance, as well as its practical application.

Independent variables with hypothesised significance to Price 1. Room Type: This variable represents categories such as "Entire home/apartment," "Private room," and "Shared room." Expected Relationship: Entire homes/apartments are expected to be the most expensive as they offer more facilities and privacy. Private rooms are expected to be the second most expensive, while shared rooms are expected to have the lowest price rates.

2. Property Type: This variable refers to the type of property, such as a house, apartment, guest suite, hotel room, and others. Expected Relationship: Houses, guest suites, and hotel rooms are expected to have higher prices compared to smaller or unique property types.

3. Number of Bedrooms: This determines the size and capacity of the property. Expected Relationship: Properties with more bedrooms are likely to cost more since they can accommodate larger groups.

4. Review Scores Rating: This variable calculates the average rating from previous guests. Expected Relationship: Higher review scores are expected to correlate with higher prices due to increased demand and perceived quality.

5. Number of Reviews: This variable indicates the popularity and credibility of the listing. Expected Relationship: More reviews can lead to higher demand and potentially higher prices, though the relationship may not be directly proportional.

6. Accommodates: This variable refers to the maximum number of guests that can be accommodated. Expected Relationship: Listings that can accommodate more guests are likely to have higher prices due to their ability to host larger groups.

7. Availability (30 Days): This variable estimates the availability of the accommodation in the short term. Expected Relationship: High availability might indicate readiness to host guests and could suggest lower demand, potentially leading to lower prices.

Regression Methodology The regression analysis in this paper was performed using the Ordinary Least Squares (OLS) technique, which estimates the relationship between the independent variables and the dependent variable while accounting for the effect of other variables.

Bayesian Analysis Bayesian Correlation: This was used to determine the strength of the relationships and provided a probabilistic view of the variables. Bayesian ANOVA: This compared different property types and assessed the variability of the estimates.

Results The regression analysis provided the following results: Model Summary R-Squared: 0.446

This means that 44.6% of the total variation in the prices of the listed properties can be explained by the independent variables in the model. Although this is not a very high fit, it implies that there are other variables outside the model that affect the prices. Adjusted R-Squared: 0.976

 The adjusted R-squared accounts for the number of predictors in the model, thus giving more credibility to the model's effectiveness. It explains 97% of the variance.

ANOVA F-Statistic: 57.865 (p-value: 0.0001)

 This is a confirmation that the overall regression model is statistically significant, which implies that some of the predictors have a significant influence on the dependent variable.

Coefficients and Interpretations Key Findings Accommodates: It was found that this was the strongest predictor of price. Indeed, each extra guest increases the value by $25.17, highlighting that guest capacity is critical for determining the listing price. Review Scores Rating: Posted significant correlation. Higher ratings mean increased demand, resulting in higher prices. Number of Rooms: The results indicate that properties with more rooms are associated with mid or low-grade accommodations, typically decreasing prices. Bedrooms: Properties with more bedrooms tend to accommodate more guests, which can increase the price. Availability (30 Days): Insignificant. There was little variance with short-term availability, suggesting that other factors affect pricing more.

R-Squared Interpretation The model fit can be considered moderate since the R-squared value is 0.446. This implies that the model captures a significant portion of the price variability, but there are other factors, such as neighborhood and proximity to places of interest, that could enhance the model.

Bayesian Insights Bayesian Correlation: As expected, the correlation of accommodates showed a high rate of correspondence (mean = 0.776). The theoretical and marginal significance matched well. Bayesian ANOVA: A relative decrease in availability is implied by the themes found when product forms are maintained based on the same angle. P-P Plot: Figure 5 indicates that the residuals are normally distributed, confirming the validity of the model.

Discussion Main Findings The analysis of Airbnb listing prices revealed the following key factors influencing price

Strongest Predictors: Accommodates Lastly. The capacity of quests was the most defining factor for pricing such that more capacity means higher price from the list The prices also Rose by $25.17 for every conditional guest capacity. Consequently, guest copany serves as an essentiul factor that defines the value of a listing by pointing to a relatiobetween property costing and perceived value, where larger properties that may accommodate groups or families are considered to be more valuable. Review Scores Rating by Guests were also a factor with an inverted relationship between them and pushes the higher rating, the higher the price. Mean index acore. For each and every increase of one point was a corresponding increase in price by $26.36. The Means that guests are willing to spend more on listings which are ranked well because ratings suggest the quality of the listing and its host

Surprising and Insignificant Results: Bedrooms Although highly related to size and the capacity of a property, the number of bedrooms in a potential home was not a commanding factor for the cost. This may be so because the accommodates variable gives the size of the property in a much more direct sense. Availability (30 Days) Avallability over the short run did not influence the intensity to list prices. It may be because tenants are more inclined to look for various features of accommodation instead of selectivity when they search for listings Number of Reviews. Although statistically insignificant only in the last ansysis, the sign of the coefficient was negative. This productive results reveal improper such that listings that are more budget-conscious or midrange accommodations are more relevant for quests while the more expensive 'Premium Listings have fewer but higher average occupancy rates The significance value of the overall model with R-square of 0.446 presents that the ovarall model accounts for slightly above 45% variability within the dependent variable However, this draws attention to the existence of other unmeasured characteristics which may also include location, amenities or seasonality.

Implications for Renters 1 Target Smaller Listings for Budget-Friendly Options Indicates that there is a proportionate direct relationship between the number of guests a listing can comfortably accommodate and the price it charges. Those that offer only the two bed rooms can save a lot of money on booking these houses by preferring those that offer lower occupancies.

2. Review Scores as a Quality Benchmark Selecting the listing tend to look for the high rating score and it means that a quest enjoyed their stay even at a higher cost. Potential customers who may be tight on their dudget may filter a property with moderately high ratings or more (4.5 and above)

3. Ignore Availability as a Pricing Factor Again the type of availability within the 30 days does not impact the cost of the product The use of pricing filters allows renters to work through thousands of listings with high availability without the fear of having to pay through their nose.

4 Evaluate Listings with High Renew Counts Such listings can have a larger number of reviews, so it can be more worthwhile for renters who don't look for luxury stay to book a place with the largest number of reviews, but a relatively low price.

Implications for Landlords 1 Optimize Guest Capacity The approach most effective for raising prices has been found to be a simple one allowing listings to take more guests at a time. For onstance facilities like suites or bunk beds can be a sufficient reason for the higher charges and large group potential.

2. Focus on Improving Guest Reviews Increasing reader satisfaction in terms of service provision and facilities' quality improve chances of a listing. The higher ratings are matched with high prices, which makes it useful for landlords in their leasing business.

3 Highlighting premium Features

Information about each property that can be listed, including its photos and descriptions, should focus on aspects that buyers are willing to pay more for, such as the presence of sports facilities. This could also attract higher-grade customers who are willing to spend a little extra.

4. Reevaluating Bedroom Count Focus Since the number of bedrooms influences rental prices, landlords should aim to make the most out of the space instead of adding bedrooms that may not be useful.

5. Maximize Reviews Strategy With more reviews signaling the position of the landlords, not only can they attract more guests, but they can also invite others to do the same. Guests should be asked to provide detailed positive reviews of the listing and its price.

From this analysis, the following section presents a discussion of the key strategies that may be of importance for both the renters and the landlords. For renters, knowing how guest capacity is connected with the rating and the price will help make better decisions. It was also seen that the most critical ways in which landlords can optimize pricing for their property and generate the highest revenue include focusing on guest capacity and enhancing the scores of reviews. With these observations, the interests of stakeholders will be better addressed in facing the stiff competition in the short-term rental business.

Limitations Scope of the Analysis The findings of this analysis apply specifically to the context of the data gathered in this study regarding Airbnb listings. The situation in a few major cities might not be directly applicable to other markets or geographical areas. Other variables like geographical location, cultural/ethnic issues, and regulatory policies influence the Airbnb price, but these characteristics were absent from this dataset. For instance, properties listed in big cities that are considered prime markets, such as New York or San Francisco, may exhibit different price patterns than properties listed in less popular areas such as rural regions. Moreover, issues of seasonal fluctuations, which are typical in vacation rental settings, are not discussed, thus making the conclusions relevant only for year-round pricing. Correlation vs. Causation While the regression model examines how and to what extent the independent variables vary with the listing prices, the analysis does not show causality. For example, there is evidence of a positive correlation between review scores and prices; however, there is no evidence that a positive change in review scores will automatically result in an increase in price. This idea can be quite debatable because other factors like better location or better amenities are likely to combine with better review ratings and higher prices as well. It should be noted that for the establishment of causality, either an experimental design or the use of complex statistical methods would be appropriate.

Data-Specific Limitations Multicollinearity: There are some variables, such as the number of bedrooms and accommodates, that are related. This causes variables to share their impact, hence affecting their precise coefficients. This multicollinearity could obscure the true relationship between these variables. Potential Missing Variables: Attributes such as location, proximity to attractions, amenities like pools and Wi-Fi, and host characteristics like response time were all missing from the given dataset but greatly affect prices. Outliers: Some of the extreme prices were taken out of the analysis. Sometimes the remaining outliers may be highly influential and distort the findings, especially in specific categories like luxury goods. Data Snapshot: The study only takes a point-in-time cross-section of the listings. Thus, there might be price changes over time that could be seasonal or due to market fluctuations.

Conclusion In this paper, factors affecting Airbnb listing prices were discussed, with accommodates and review scores rating having the highest coefficients in the high prices. Notably, other variables such as the number of bedrooms and short-term listing did not exert material influence on the prices, perhaps because they are combined with other predictors or context-contingent factors. For renters, it becomes important to filter smaller listings and pay close attention to reviews to get the right proportions of both the cost and quality of the apartment. Again, for the landlords, increasing the number of guests to the house and improving the standard to perhaps get more guests to give good reviews are some of the most effective ways of making more money. Subsequent studies should consider aspects such as geographical location and time of the year, as well as available services, to get accurate factors that affect the prices. Extension of these studies employing features of machine learning models or causal inference techniques could enhance the forecasts and relationship causality. Although the study adds to the relatively limited literature on global short-term rental markets on platforms, broadening the scope of analysis by adding listings from other markets and time horizons would strengthen the external validity of findings and provide more texture to the global story of short-term rentals.

International differences

Australia

There are approximately 7,000 B&Bs in Australia.

The B&B industry in Australia generates about $132 million in annual revenue.

China

In China, expatriates have remodelled traditional structures in quiet picturesque rural areas and opened a few rustic boutique hotels with minimum amenities. Most patrons are foreign tourists but they are growing in popularity among Chinese domestic tourists.

India

In India, the government is promoting the concept of bed & breakfast. The government is doing this to increase tourism, especially keeping in view the demand for hotels during the 2010 Commonwealth Games in Delhi. They have classified B&Bs into two categories: Gold B&Bs, and Silver B&Bs. All B&Bs must be approved by the Ministry of Tourism, which will then categorize them as Gold or Silver based on a list of predefined criteria.

Ireland

In Ireland, B&Bs can be townhouses, farmhouses, or country houses. There are about 3,000 B&Bs spread throughout the country, 750 of which are certified for meeting quality standards by Fáilte Ireland.

Israel

Rural tourism in Israel includes accommodations ranging from basic converted buildings to luxurious log cabins (Zimmers) equipped with modern amenities.

The Israeli B&B is known as a zimmer (German for 'room'). All over the country, but especially in northern Israel (Galilee, Upper Galilee, and the Golan Heights), the zimmer has developed into an extensive industry. This industry began to develop in the 1990s, when agriculture became less profitable, and many families with farms in moshavim, kibbutzim, farms, and even in cities decided to try their luck in the business of hospitality. In the last decade, bed and breakfasts have also emerged in southern Israel's Negev region.

Italy

In Italy, regional law regulates B&Bs. There is a national law "Legge 29 marzo 2001, n. 135" but each region maintains a specific regulation. Each region can adopt different regulations but they must observe the national law on Tourism (Law N° 135 /2001).

Carriage B&B Hinto Panzio in Transylvania, Odorheiu Secuiesc, Romania

Japan

The Japanese equivalent of this sort of hospitality business is the minshuku, which offers dinner as well as bed & breakfast in the tradition of the ryokan.

United Kingdom

Little Langford Farmhouse, Salisbury, Wiltshire

There are numerous B&Bs found in seaside towns, the countryside as well as city centres.

B&Bs are graded by VisitBritain and the AA on a star system. 3-, 4- and 5-star establishments have a higher standard. A majority of B&Bs in the UK have en suite facilities.

United States

Front yard of a B&B in the Dupont Circle neighborhood of Washington, D.C.

There are approximately 17,000 B&Bs in the United States. Bed and breakfasts are often private family homes and typically have between four and eleven rooms, with six being the average.

See also

• Airbnb
• Inn
• Hostel