BACKGROUND

1. Technical Field

The present disclosure relates to price tracking, valuation, and negotiation optimization.

2. Introduction

The price of a hospitality product, such as a hotel room or meeting space, may be very subjective and may vary based on variables that are not directly related to the product, supply, or demand. For example, an offer price or an accepted price may be based on unpredictable or inconsistent properties which may undermine the costumer's confidence in proposed or accepted rates. This may cause consumers to be unsure if they are getting a fair price on the space. Thus it may be advantageous to use automated machine learning software to monitor the proposed rate and the final negotiated rate of various spaces based on objective information. Proposed, therefore, is a tool that associates historical pricing points to data points about the negotiation or the property. For example, the desirability of the location, the time of year, the facilities available in the space, the size of the space, the services included, the cancellation policy, information about parties to the negotiation (i.e. their location, net worth, or size), how long the parties have been negotiating, the time of the negotiation, the size of the account, the number of customers that accepted bid in the last period year, or the proximity of the customer's offices to the property. A buyer facing tool may offer users the ability to predict pricing to aid potential buyers in their negotiations.

SUMMARY

Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein.

Disclosed are systems, methods, and non-transitory computer-readable storage media a technical solution to the technical problem described. A system, comprising: a data collector configured to collect a historical proposed rate, a historical accepted rate, a historical product characteristic, and a historical customer characteristic; a data cleaner configured to: receive the historical proposed rate, the historical accepted rate, the historical product characteristic, and the historical customer characteristic from the data collector; convert the historical proposed rate and the historical accepted rate to a current currency value; remove outliers from a converted historical proposed rate and a converted historical accepted rate; assign the converted historical proposed rate and the converted historical accepted rate to a clean numeric variable; assign a historical product characteristic and a historical customer characteristic to characteristic variables; generate calculated variables; generate a numerical rate for characteristic variables based on new calculated variables; characteristic variables, and numerical variables; divide the numerical rate into a first data group, a second data group, and a third data group; perform a training on the first data group; perform a validation on the second data group; and perform a testing on the third data group; model builder configured to build a prediction model based on the training, validation, and testing; rate predictor configured to: receive the prediction model; receive inputs from a user about a channel characteristic, a property characteristic, and a season; input the channel characteristic, the property characteristic, and the season into the prediction model; generate a prediction from the prediction model; and output the prediction to the user.

A method comprising: collecting a historical proposed rate, a historical accepted rate, a historical product characteristic, and a historical customer characteristic; converting the historical proposed rate and the historical accepted rate to a cur-rent currency value; removing outliers from a converted historical proposed rate and a converted historical accepted rate; assigning the converted historical proposed rate and the converted historical accepted rate to a clean numeric variable; assigning a historical product characteristic and a historical customer characteristic to characteristic variables; generating calculated variables; generating a numerical rate for characteristic variables based on new calculated variables; characteristic variables, and numerical variables; dividing the numerical rate into a first data group, a second data group, and a third data group; performing a training on the first data group; performing a validation on the second data group; performing a testing on the third data group; building a prediction model based on the training, validation, and testing; receiving the prediction model; receiving inputs from a user about a channel characteristic, a property characteristic, and a season; inputting the channel characteristic, the property characteristic, and the season into the prediction model; generating a prediction from the prediction model; and outputting the prediction to the user.

A computer-readable storage medium configured as disclosed here may store instructions which, when executed by a computing device, causes the computing device to perform a method comprising: determining a property characteristic about a property; assigning the property characteristic a numerical property value using a pricing model; determining a channel characteristic about a channel; assigning the channel characteristic a numerical channel value using the pricing model; determining a negotiation characteristic about a negotiation; assigning the negotiation characteristic a numerical negotiation value using the pricing model; determining a season characteristic about a season; assigning the season characteristic a numerical season value using the pricing model; determining a term characteristic about a term; assigning the term characteristic a numerical term value using the pricing model; receiving a paid price from a user device; assigning the price paid to the property characteristic, the channel characteristic, the negotiation characteristic, the season characteristic, and the term characteristic; removing outliers from the property characteristic, the channel characteristic, the negotiation characteristic, the season characteristic, and the term characteristic; dividing the property characteristic, the channel characteristic, the negotiation characteristic, the season characteristic, and the term characteristic into a first group, a second group, and a third group; performing training on the first group; performing validation on the second group; performing testing on the third group; updating the pricing model based on the training, validation and testing.

