The Application of Regression Analysis for Estimating the Market Value of Commercial Real Estate

The transaction price of a land property with commercial buildings depends on both its quantitative and qualitative attributes. Quantitative attributes include surface areas of plots of land and usable floor spaces of premises and buildings with various intended purposes, as well as values of rents. Qualitative attributes are represented by the global attributes of these properties.In the analysis of the land property market with commercial buildings, all pairs that relate a transaction price to individual attributes are considered. The market value prediction is based on multiple regression analysis for a two-dimensional random variable, represented by the price and the predetermined attribute. The final market value of the property being valued is calculated as the weighted average of the market values predicted for each attribute.This research paper presents the procedure for determining the market value of land with commercial buildings, which falls within the method of statistical analysis of the market. The derived formulas and substantively justified algorithms may be the basis for market analysis and estimation of the market value of such land. This procedure has been thoroughly verified using two practical numerical examples.

Dynamic Sales Price Control Model for Exclusive Exquisite Products within a Time Interval

When information regarding the effective evaluation of the value of exquisite products is lacking, the market demand function for such products at a given time point is affected by the diffusion of historical transaction price information before the time point. This is because historical transaction prices play an active role in influencing the internal reference price (IRP) of customers, and the continuous diffusion of historical transaction price information leads to the continuous correction, adjustment, and updating of customers’ IRPs. Given the varying rates of such information diffusion, the speed at which customers adjust their IRPs also varies across individuals and contexts. By considering the exponential distribution of potential customers’ IRPs as an example to establish the dynamic demand function that considers the effect of historical transaction prices, this paper discusses the effect of different information diffusion rates on the demand function at a time point. On the basis of this demand function, a sales price control model that maximizes the discounted profitability for businesses in the patent term of an exquisite product is then constructed to provide businesses with an operation method to cultivate prices and increase profits.

Rubik’s Cube Topology Based Particle Swarm Algorithm for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g. renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as, reducing power loss, stabilizing the main power grid, etc. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush-Kuhn-Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubiks Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubiks Cube topology based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology based PSO and its scalability on larger scale applications.